News

  • 5/25/2022: New paper published in Frontiers in Radiology entitled "PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration". Congrats to recent SMILE Lab PhD graduate Peng Liu and undergraduate graduate Garrett Fullerton! [Link]
  • 5/5/2022: Our paper entitiled "DOMINO: Domain-aware Model Calibration in Medical Image Segmentation" is early accepted by MICCAI in Singapore Sep. 18-22, 2022 (early acceptance rate=13%)! Congrats to Skylar Stolte!
  • 5/3/2022: Dr. Fang was interview by ABC WCJB TV Tech Tuesday on her research using artificial intelligence (AI) to detect Alzheimer's Disesase. [Interview]
  • 4/14/2022: Congratulations to SMILE Lab member Garrett Fullerton on winning the Outstanding Research Award at the BME Undergraduate Research Day! [News]
  • 3/11/2022: A new collaborative $10.7M P01 entitled "Multi-Scale Evaluation and Mitigation of Toxicities Following Internal Radionuclide Contamination" (PI: Gayle E. Woloschak) is funded by NIH NIAID!
  • 3/4/2022: Dr. Fang is selected to receive the Herbert Wertheim College of Engineering 2022 Faculty Award for Excellence in Innovation! [News]
  • 3/1/2022: Dr. Fang is appointed as the Associate Director in the UF Intelligent Critical Care Center (IC3).
  • 2/16/2022: Dr. Fang will serve as Topic Editor on "Women In Brain Imaging and Stimulation" with Frontiers in Human Neuroscience Journal. Welcome to submit to this exciting topic!
  • 2/12/2022: Dr. Fang will serve as Area Chair of the 25th International Conference of Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 in Singapore. Call for papers for this top conference in MIA!
  • 2/2/2022: Dr. Fang and collaboratorsjoined forces with Nvidia scientists and OpenACCorg to accelerate brain science at the Georgia Tech GPU Hackathon. The StimulatedBrain AI@UF team drastically improved their processing time for evaluating datapoints collected from an individual brain. [News] [Twitter]
  • 01/18/2022: Dr. Fang delivers an invited talk entitled "Modular machine learning for Alzheimer's disease classification from retinal vasculature" at University of Oxford "Artificial Intelligence for Mental Health" seminar series.
  • 01/07/2022: Dr. Fang is interviewed by Ivanhoe on her collaborative work on Aritifial Intelligence to prevent dementia. [News]
  • 01/04/2022: Dr. Fang gives an invited talk entitled "Artificial Intelligence for Cognitive Aging - Novel Diagnosis and Precision Intervention" at the University of Florida Institute of Aging.
  • 12/15/2021: Congrats to SMILE Lab PhD Dr. Peng Liu graduated!
  • 12/14/2021: Dr. Fang receives the BME Departmental Faculty Research Excellence Award! [News]
  • 12/7/2021: A new paper on Artificial Intelligence to predict dementia entitled "Baseline neuroimaging predicts decline to dementia from amnestic mild cognitive impairment" is published in Frontiers in Aging Neuroscience! [Paper] [News]
  • 11/30/2021: Kudos to SMILE PhD Peng Liu on winning the UNIQUE-IVADO Award for Best Abstract (Undergraduate and Graduate Level) at Montreal AI& Neuroscience (MAIN) Conference! [Video] [News]
  • 11/3/2021: Our lab's new AI paper entitled "Unraveling Somatotopic Organization in the Human Brain using Machine Learning and Adaptive Supervoxel-based Parcellations" is accepted by NeuroImage today! Kudos to Kyle See as a leading first author! [Link]
  • 10/29/2021: Our lab's new paper on AI in domain adaptation entitled "CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation" is published in NeuroComputing today! Kudos to Peng and Charlie! [Link]
  • 10/28/2021: Congratulations to SMILE Lab member Peng Liu on successfully defending his Ph.D. dissertation entitled "Biology and Neuroscience-Inspired Deep Learning"! Congrats, Dr. Liu!
  • 10/28/2021: New collaborative paper on "Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks" published in Theoretical and Computational Fluid Dynamics! [Link]
  • 10/22/2021: New publication on AI in computational fluid dynamics entitled "Rotational and Reflectional Equivariant Convolutional Neural Network for data-limited applications: Multiphase Flow demonstration" in the Journal of Physics of Fluids is now online! [Link]
  • 10/9/2021: Dr. Fang will serve as session chair for Optical Imaging Session of the Annual Meeting of Biomedical Engineering Society (BMES) on Oct. 6-9, 2021 at Orlando, FL.
  • 9/30/2021: A new collaborative grant entitled "Creation of an intelligent alert to improve efficacy & patient safety in real time during fluoroscopic guided lumbar transforaminal epidural steroid injection" is funded by the I. Heermann Anesthesia Foundation.
  • 9/29/2021: Dr. Fang serves as the session chair of Image Reconstruction Session at MICCAI 2021.
  • 9/21/2021: A new 5-year U24 grant ($2.5M) entitled "Southern HIV and Alcohol Research Consortium Biomedical Data Repository" is funded by NIH NIAAA!
  • 9/10/2021: A new 5-year P01 grant ($6.6M) entitled "Interventions to improve alcohol-related comorbidities along the gut-brain axis in persons with HIV infection" is funded by NIH NIAAA! [News]
  • 9/9/2021: A new 4-year SCH grant ($1.2M) entitled "Collaborative Research: SCH: Trustworthy and Explainable AI for Neurodegenerative Diseases" is funded by National Science Foundation! Dr. Fang is Co-PI on this Trustworthy and Explanable Artificial Intelligence (AI) award. [NSF] [News]
  • 9/9/2021: A new 5-year P01 grant ($1.5M to UF) entitled "SHARE Program: Innovations in Translational Behavioral Science to Improve Self-management of HIV and Alcohol Reaching Emerging adults" is funded by NIH NIAAA!
  • 8/26/2021: Three abstracts are accepted by Society of Neuroscience (SfN) Annual Meeting to be held November 8-11 virtual and November 13-16, 2021 in Chicago:
    • Emergence of emotion selectivity in deep neural networks trained to recognize visual objects
    • A deep neural network model for emotion perception
    • Machine learning defined precision tES for improving cognitive function in older adults
    Congrats to Peng and Alejandro!
  • 8/24/2021: A new paper entitled "Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks" is accepted by Theoretical and Computational Fluid Dynamics!
  • 8/23/2021: Welcome two new Ph.D. students Charlie Tran and Seowung Leem to join SMILE Lab!
  • 8/17/2021: Kudos to SMILE Lab PhD student Charlie Tran who has been selected into the highly competitive NextProf Pathfinder 2021 to be held during October 17-19, 2021 on the campus of University of Michigan, Ann Arbor, co-sponsored by the University of California, San Diego. Congrats Charlie! [News]
  • 8/11/2021: Congratulations to SMILE lab undergraduate student Gianna Sweeting on being selected to receive the John & Mittie Collins Engineering Scholarship from Herbert Wertheim College of Engineering (HWCOE)! [News]
  • 7/30/2021: A new collaborative 5-Year R01 grant ($2.3M) entitled "Acquisition, extinction, and recall of attention biases to threat: Computational modeling and multimodal brain imaging" has been funded by NIH NIMH!
  • 7/15/2021: Our AI research on precision dosing for preventing dementia has been reported by UF News, WPLG Local 10 News (ABC-affiliated local TV), WCJB TV (ABC/CW+ affiliated local), and The Alligator.
  • 6/14/2021: Dr. Fang presented as an invited speaker at the Cleveland Clinic-National Science Foundation Workshop "The Present and the Future of Artificial Intelligence in Biomedical Research".
  • 6/4/2021: NeuroAI T32 Machine Learning Workshop taught by Dr. Fang has successfully completed! [News]
  • 5/14/2021: New 4-Year MPI (Fang & Woods) RF1 grant ($2.9M) on Artificial Intelligence for transcranial direct current stimulation (tDCS) in remediating cognitive aging has been funded by NIH! [News]
  • 5/2/2021: Congrats to SMILE PhD student Skylar Stolte on passing her Doctoral Comprehensive Exam!
  • 4/7/2021: Congratulations to SMILE undergraduate student and incoming Ph.D. student Charlie Tran on receiving the McNaire Graduate Assistantship!
  • 3/18/2021: New 5-year ($5M) NIH U01 grant on New AI tool to improve diagnosis of Parkinson’s and related disorders. [News]
  • 3/1/2021: Congratulations to SMILer undergraduate Gianna Sweeting for being selected by the Univerity Scholar Program!
  • 2/26/2021: Dr. Fang's work AI in Parkinson's Disease dignosis via eye scans has been reported by The Washington Post.
  • 2/25/2021: Congratulations to SMILer undergraduate Gianna Sweeting for being selected as a Fernandez Family Scholar by the Herbert Wertheim College of Engineering for her excellent progress and interest in research and graduate studies. [News][Twitter]
  • 2/18/2021: Dr. Fang has been selected as Aspiring PI to attend 2021 NSF SCH PI Workshop: Smart Health in the AI and COVID Era.
  • 2/18/2021: SMILE lab PhD student Peng Liu has been selected by NSF to attend the 2021 NSF SCH PI Workshop: Smart Health in the AI and COVID Era.
  • 2/2021: Dr. Fang will serve as Track Chair for BMES 2021
  • 2/2021: Dr. Fang will serve as Program Committee for MICCAI 2021.
  • 1/25/2021: Congrats to Kyle See and Rachel Ho whose joint paper on "TL1 Team Approach to Predicting Response to Spinal Cord Stimulation for Chronic Low Back Pain" has been accepted by Translational Science 2021!
  • 12/2020: Dr. Fang, together with Dr. Mingzhou Ding and Dr. Andreas Keil have been awarded UF Research Artificial Intelligence Research Catalyst Fund on the project "VCA-DNN: Neuroscience-Inspired Artificial Intelligence for Visual Emotion Recognition"! [Link]
  • 12/15/2020: Our paper on "Modular machine learning for Alzheimer's disease classification from retinal vasculature" has been accepted by Nature Scientific Reports! [Link]
  • 12/2020: Dr. Fang has been interviewed by Forbes, RSNA, Diagnostic Imaging on her research about AI for Parkinson's Disease Diagnosis via Eye Exam.
  • 11/2020: Dr. Fang is interviewed by UFII on AI for brain functions. [Link]
  • 10/30/2020: Paper published in Brain Simulation on Machine learning and individual variability in electric field characteristics predict tDCS treatment response". [PDF]
  • 10/14/2020: Congratulations to SMILer Gianna Sweeting on receving the Engineering College Scholarship from the Herbert Wertheim College of Engineering (HWCOE)! [News]
  • 9/4/2020: Congratulations to BME alumna Yao Xiao Ph.D on receiving the prestigious 2020 BMES Career Development Award! [News]
  • 8/5/2020: Congrats on Skylar Stolte on her abstract entitled "Artificial Intelligence For Characterizing Heart Failure In Cardiac Magnetic Resonance Images" being accepted by American Heart Association Scientific Sessions 2020!
  • 7/16/2020: Our paper on "Physiological wound assessment from coregistered and segmented tissue hemoglobin maps" is published at Journal of the Optical Society of America A. It is a collaborative work with Dr. Anuradha Godavarty at Florida International University. [Paper]
  • 7/6/2020: Congratulaions to SMILE Member PhD student Peng Liu on receving UFII Graduate Student Fellowship on his project on Neuroscience-Inspired Artificial Intelligence![News]
  • 6/25/2020: Yao Xiao presented at the 2020 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM) on 'Multi-Series CT Image Super-Resolution by using Transfer Generative Adversarial Network'! [Link]
  • 6/12/2020: SMILer Kyle B. See is highlighted in BME Student Spotlight! [News]
  • 5/30/2020: Congratulations to Skylar Stolte for her paper, 'A Survey on Medical Image Analysis in Diabetic Retinopathy', being accepted for publication in Medical Image Analysis! [Link]
  • 5/21/2020: Congratulations to Kyle See for passing the PhD Departmental Comprehensive Exam!
  • 5/4/2020: Garrett Fullerton and Simon Kato have been selected for NSF REU.
  • 4/5/2020: Yao Xiao presented at the IEEE International Symposium on Biomedical Imaging (ISBI'20) on 'Transfer-GAN: Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Networks'! [Paper]
  • 4/30/2020: Yao Xiao has been selected as a Graduate Student Commencement Speaker at the College of Engineering Graduation Ceremony. [News] [Speech beings at 16:28]
  • 4/7/2020: SMILer Charlie Tran has been selected to participate in the University of Florida's Ronald E. McNair Post-Baccalaureate Achievement Program. [News]
  • 4/1/2020: SMILE Lab Alumnus Daniel El Basha has been awarded the NSF Graduate Research Fellowship! [News]
  • 3/25/2020: Yao Xiao on successfully defending her PhD dissertation!
  • 2/25/2020: SMILer Garret Fullerton has been accepted into UF University Scholars Program to work on machine learning for medical image optimization. [News]
  • 2/17/2020: Yao Xiao presented at the SPIE Medical Imaging (SPIE MI'20) on 'Transfer generative adversarial network for multimodal CT image super-resolution'! [Talk]
  • 2/7/2020: Congrats to Yao Xiao for her abstract, 'Multi-Series CT Image Super-Resolution by using Transfer Generative Adversarial Network', being accepted for presenting at the 2020 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM)! [Paper]
  • 1/6/2020: Congrats to Yao Xiao for her paper, 'Transfer-GAN: Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Networks', being accepted for publication in the IEEE International Symposium on Biomedical Imaging (ISBI'20), and awarded the ISBI (NIH, NIBIB, NCI-funded) Student Travel Award, UF GSC Student Travel Award! [Paper]
  • 12/4/2019: Dr. Fang presented at the Annual Conference of Radiological Society of North America (RSNA) on “Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Network” with Ph.D. student Yao Xiao as the leading author. [Link]
  • 10/16/2019: Congrats to Yao Xiao for her abstract, 'Transfer generative adversarial network for multimodal CT image super-resolution', being accepted for presenting at the SPIE Medical Imaging (SPIE MI'20)! [Paper]
  • 9/9/2019: Dr. Fang receives an NSF award entitled "III:Small: Modeling Multi-Level Connectivity of Brain Dynamics". [News]
  • 8/30/2019: Congrats to Peng Liu on passing his dissertation qualification!
  • 8/26/2019: Kudos to Peng Liu and Dr. Fang for their first patent at UF. A Software Using a Genetic Algorithm to Automatically Build Convolutional Neural Networks for Medical Image Denoising! [News]
  • 8/20/2019: Congrats to our STTP students on their fantastic project presentations!
  • 7/26/2019: 'Development and Validation of Automated Imaging Differentiation in Parkinsonism: A Multi-Site Machine Learning Study' accepted for publication in The Lancet Digital Health. Congrats to SMiLE Lab and Dr. David Vaillancourt!
  • 8/20/2019: Two SSTP high school students Imaan Randhawa and Aarushi Walia successfully completed their summer research in SMiLE Lab under the supervision of Dr. Fang and PhD student Yao Xiao.
  • 6/28/2019: Dr. Fang recieves UF Informatics Institute Seed Fund Grant for research in smartphone based Diabetic Retinopath detection. [News]
  • 6/13/2019: Kyle See is awarded an NIH CTSI TL1 Predoctoral Fellowship to study methods and mechanisms of cognition and motor control through data science. [News]
  • 5/10/2019: Dr. Fang receives collaborative CTSI Pilot Award to study cancer therapy-induced cardiotoxicity. [News]
  • 5/7/2019: Congrats to Yao Xiao for successfully defending her proposal
  • 4/16/2019: Congrats to Peng Liu for his paper ,'Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising', being accepted for publication in Medical Image Analysis! [News]
  • 4/16/2019: Kudo to Yao Xiao for her paper, 'STIR-Net: Spatial-Temporal Image Restoration Net for CTPerfusion Radiation Reduction', being accepted for publication in Frontiers in Neurology, section Stroke. [News]
  • 4/16/2019: SMiLE Lab's Senior Design group presents their final project for Smartphone Based Diabetic Retinopathy Diagnosis.
  • 3/7/2019: Maximillian Diaz accepted to UF University Scholars Program to work on retina based Parkinson's diagnosis. [News]
  • 1/10/2019: Dr. Fang named a senior member of IEEE. [News]
  • 5/17/2018: Dr. Fang awarded University of Florida Informatics Institute and the Clinical and Translational Science Institute (CTSI) pilot funding for precision medicine. [News]
  • 4/24/2018: SMiLE Lab recieves two first place awards at the Diabetic Retinopathy Segmentation and Grading Challenge. [News]
  • 3/26/2018: Akash and Akshay Mathavan accepted to UF University Scholars Program to work on environmental risk factors for ALS. [News]
  • 10/30/2017: Two master students, Yangjunyi Li and Yun Liang, join SMILE Group. Welcome!
  • 10/1/2017: Two UF BME senior students Kyle See and Daniel El Basha join SMILE lab to work on big biomedical data anlytics research supported by NSF REU. Welcome! [News]
  • 08/2017: Yao is awarded MICCAI 2017 Student Travel Award. Congrats to Yao! [News]
  • 08/2017: Four papers from SMILE Lab will be presented at BMES 2017 in Pheonix, Arizona.
  • 08/2017: SMILE Lab will move to J. Crayton Pruitt Family Department of Biomedical Engineering at University of Florida.
  • 07/2017: One paper is accepted by Machine Learning and Medical Imaging (MLMI) Workshop at MICCAI 2017. Congrats to Yao!
  • 07/16/2017: Dr. Fang is invited to give a talk at the 4th Medical Image Computing Seminar (MICS) held at Shanghai Jiaotong University, Shanghai, China.
  • 06/2017: Yao won the NSF Travel Award to attend CHASE 2017 in Philadelphia, PA. Congrats to Yao! [News]
  • 05/2017: Dr. Fang is invited by NSF as a panelist of Smart and Connect Health.
  • 05/2017: One paper is accepted by CHASE 2017. Congrats to Yao!
  • 04/2017: One paper is accepted by MICCAI 2017. Congrats to Ling!
  • 04/2017: Dr. Fang is invited to give a talk at the Society for Brain Mapping and Therapeutics Annual Meeting in Los Angeles, CA.
  • 03/2017: Dr. Fang attended NSF Smart and Connected Health PI Meeting held in Boston, MA.
  • 10/2016: PhD student Maryamossadat Aghili has been awarded travel grants from NIPS WiML 2016 Program Committee and FIU GPSC to attend WiML of NIPS 2016 in Barcelona.
  • 09/2016: Dr. Fang is invited by NIH to review in BDMA study section.
  • 09/2016: One paper is accepted by Pattern Recognition!
  • 08/2016: Dr. Fang is selected as an Early Career Grant Reviewer by NIH.
  • 08/2016: SMILE REU and RET students and teachers won the 2016 Best REU and RET Poster Awards at SCIS of FIU.
  • 07/2016: Our paper "Abdominal Adipose Tissues Extraction Using Multi-Scale Deep Neural Network" is accepted by NeuroComputing!
  • 04/2016: PhD student Maryamossadat Aghili has been awarded travel grants to attend Grad Cohort Conference (CRAW).
  • 05/2016: Dr. Fang's research appeared on FIU News: Professor uses computer science to reduce patients' exposure to radiation from CT scans!
  • 05/2016: Dr. Fang has been selected to receive the 2016 Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities!
  • 05/2016: Dr. Fang received funding from NSF on CRII (Pre-CAREER) Award on "Characterizing, Modeling and Evaluating Brain Dynamics"!
  • 04/2016: Our paper "TENDER: TEnsor Non-local Deconvolution Enabled Radiation Reduction in CT Perfusion" is accepted by NeuroComputing!
  • 04/09/2016: Dr. Fang is awarded the first Robin Sidhi Memorial Young Scientist Award from the Annual Congress of Society of Brain Mapping and Therapeutics for recognizing her continuous devotion to bridge information technology and health informatics.
  • 03/2016: Our paper ""Computational Health Informatics in the Big Data Age: A Survey" is accepted by the prestigous ACM Computing Survey!
  • 01/2016: Dr. Fang is invited by NSF as a panel reviewer of Smart and Connect Health.
  • 12/2015: Dr. Fang serves as the Publicity Chair of the 14th IEEE International Conference on Machine Learning and Applications 2015.
  • 04/2015: Dr. Fang is invited by NSF as a panel reviewer of Smart and Connect Health.

Honors & Distinctions

  • 2022
    Herbert Wertheim College of Engineering, University of Florida
  • 2021
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida
  • 2020
    NVIDIA-University of Florida
  • 2020
    For the highest-scoring abstract, Co-Authored Paper
    American Society for Clinical Pharmacology and Therapeutics Annual Meeting in Houston, TX, March 18-21, 2020.
  • 2018
  • 2018
    Top-ranked (#1) in “Fovea Detection” System, First International Diabetic Retinopathy Grading and Segmentation Challenge
    IEEE International Symposium of Biomedical Imaging
  • 2018
    Top-ranked (#1) in “Optical Disc Segmentation” System, First International Diabetic Retinopathy Grading and Segmentation Challenge
    IEEE International Symposium of Biomedical Imaging
  • 2017
    Association for Computing Machinery (ACM)
  • 2017
    Robin Sidhu Memorial Young Scientist Award
    Society of Brain Mapping and Theurapeutics
    Covered by PR Newswire, Yahoo, NEWS
  • 2016
    NSF Best REU and RET Poster Awards
    image
    Dr. Fang's mentee, Paul Naghshineh from George Washington University, won the Best REU Poster Award at the annual REU Symposium at FIU. Christian McDonald with Edda I. Rivera, teachers from Miami Jackson Senior High School and John A. Ferguson Senior High School, under Dr. Fang's mentorship, won the Best RET Poster Award. Congratulations to Paul, Christian and Edda!
  • 2016
    Ralph E. Powe Junior Faculty Enhancement Award
    Oak Ridge Associated Universities
    image
    Dr. Fang has been selected to receive the 2016 Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities (two nominations from each institute, 35 awardees out of 132 applicants).
  • 2016
    NSF IIS CRII Award on "Characterizing, Modeling and Evaluating Brain Dynamics"
    image
  • 2015
    CISE CAREER Workshop Travel Award
    National Science Foundation
  • 2014
    Hsien Wu and Daisy Yen Wu Memorial Award
    Cornell University
    image
    In recognition of the excellent progress in the academic program and high potential for a successful academic career (5 awardees out of all graduate students at Cornell University).
  • 2012
    ECE Women's Conference Travel Grant
    IBM-Cornell University
  • 2010
    Best Paper Award
    IEEE International Conference on Image Processing (ICIP)
    image
    Best Paper Award at the 17th International Conference on Image Processing, 2010. (Top 1 out of 1190 accepted papers, first author publication)
  • 2010
    Best PhD Poster Award, Cornell Engineering
    Cornell Engineering Research Conference
  • 2009
    Irwin and Joan Jacobs Fellowship
    Cornell University
    image
    Awarded to students who exemplify strength and potential in academics, service, and leadership, 2009-2010

Grants & Awards

  • 2022
    P01AI165380: Multi-Scale Evaluation and Mitigation of Toxicities Following Internal Radionuclide Contamination
    Funding: National Institute of Health, National Institute of Allergy and Infectious Diseases (NIAID)
    Amount: $10.7M ($1.95M to UF)
    Role: Co-I (PI: Gayle E. Woloschak, Northwestern University)
  • 2021
    RF1: Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study (R01-equivalent
    Funding: National Institute of Health, National Institute of Aging (NIA)
    Amount: $2.9M
    Role: Principal Investigator (MPI: Ruogu Fang & Adam Woods)
  • 2021
    Collaborative Research: SCH: Trustworthy and Explainable AI for Neurodegenerative Diseases
    Funding: National Science Foundation, Smart and Connected Health [Link]
    Amount: $1.2M ($840K to UF)
    Duration: 2021-2025
    Role: Co-Principal Investigator (PI: My Thai)
  • 2021
    U24AA029959: Southern HIV and Alcohol Research Consortium Biomedical Data Repository
    Funding: National Institute of Health, National Institute on Alcohol Abuse and Alcoholism (NIAAA)
    Amount: $2.5M
    Role: Co-Investigator (PI: Samuel Wu, Robert Cook)
  • 2021
    P01AA029547: SHARE Program: Innovations in Translational Behavioral Science to Improve Self- management of HIV and Alcohol Reaching Emerging adults
    Funding: National Institute of Health, National Institute on Alcohol Abuse and Alcoholism (NIAAA)
    Amount: $3M
    Role: Co-Investigator (PI: Samuel Wu)
  • 2021
    P01AA029543: Interventions to improve alcohol-related comorbidities along the gut-brain axis in persons with HIV infection
    Funding: National Institute of Health, National Institute on Alcohol Abuse and Alcoholism (NIAAA)
    Amount: $6.6M
    Role: Co-Investigator (PI: Robert Cook)
  • 2021
    R01: Acquisition, extinction, and recall of attention biases to threat: Computational modeling and multimodal brain imaging
    Funding: National Institute of Health, National Institute of Mental Health (NIMH)
    Amount: $2.3M
    Role: Co-Investigator (MPI: Mingzhou Ding & Andreas Keil)
  • 2021
    U01: Web-based Automated Imaging Differentiation of Parkinsonism
    Funding: National Institute of Health, National Institute of Neurological Disorders and Stroke (NINDS)
    Amount: $5.2M
    Role: Co-investigator (PI: David Vaillancourt)
  • 2020
    Artificial Intelligence Catalyst Award: VCA-DNN: Neuroscience-Inspired Artificial Intelligence for Visual Emotion Recognition
    Funding: NVIDIA - Universit of Florida
    Amount: $50,000
    Role: Principal Investigator (Co-PI: Mingzhou Ding)
  • 2020
    UFII: Biology and Cognition Inspired Deep Learning
    Funding: University of Florida
    Role: Mentor (Mentee: Peng Liu)
  • 2019
    III: Small: Modeling Multi-Level Connectivity of Brain Dynamics
    Funding: National Science Foundation
    Amount: $642,000
    Role: Principal Investigator (Co-PI: Mingzhou Ding)
  • 2019
    UFII Junior SEED Award: Multimodal Visual-Text Learning from Clinical Narrative and Image for Early Detection of Diabetic Retinopathy
    Funding: University of Florida
    Amount: $40,000
    Role: Co-Principal Investigator (PI: Yonghui Wu)
  • 2019
    UFII-CTSI Pilot Award: Toward prevention of cardiotoxicity in cancer: a multimodal approach leveraging genomics, images and clinical data
    Funding: University of Florida
    Amount: $60,000
    Role: Co-Principal Investigator (PI: Yan Gong)
  • 2019
    NIH CTSI TL1: Predicting short-term and long-term effects of spinal cord stimulation: implications for clinical practice
    Funding: University of Florida
    Role: Mentor (Mentee: Kyle B. See)
  • 2018
    UFII-CTSI Pilot Award: PrecisionDose:Personalized Radiation Dose Optimization for Multimodal Imaging
    Funding: University of Florida
    Amount: $75,000
    Role: Principal Investigator
  • 2018
    Phase I IUCRC University of Florida: Center for Big Learning
    Funding: National Science Foundation (NSF Abstract)
    Amount: $1,050,478
    Role: Senior Personnel (PI: Dapeng Wu)
  • 2016
    Ralph E. Powe Junior Faculty Enhancement Award
    Funding: Oak Ridge Associated Universities (ORAU)
    Amoung: $10,000
    Role: Principal Investigator
  • 2016
    CRII: Characterizing, Modeling and Evaluating Brain Dynamics
    Funding: National Science Foundation
    Amount: $190,991
    Role: Principal Investigator
    image

    Brain dynamics, which reflects the healthy or pathological states of the brain with quantifiable, reproducible, and indicative dynamics values, remains the least understood and studied area of brain science despite its intrinsic and critical importance to the brain. Unlike other brain information such as the structural and sequential dimensions that have all been extensively studied with models and methods successfully developed, the 5th dimension, dynamics, has only very recently started receiving systematic analysis from the research community. The state-of-the-art models suffer from several fundamental limitations that have critically inhibited the accuracy and reliability of the dynamic parameters' computation. First, dynamic parameters are derived from each voxel of the brain spatially independently, and thus miss the fundamental spatial information since the brain is connected? Second, current models rely solely on single-patient data to estimate the dynamic parameters without exploiting the big medical data consisting of billions of patients with similar diseases.

    This project aims to develop a framework for data-driven brain dynamics characterization, modeling and evaluation that includes the new concept of a 5th dimension – brain dynamics – to complement the structural 4-D brain for a complete picture. The project studies how dynamic computing of the brain as a distinct problem from the image reconstruction and de-noising of convention models, and analyzes the impact of different models for the dynamics analysis. A data-driven, scalable framework will be developed to depict the functionality and dynamics of the brain. This framework enables full utilization of 4-D brain spatio-temporal data and big medical data, resulting in accurate estimations of the dynamics of the brain that are not reflected in the voxel-independent models and the single patient models. The model and framework will be evaluated on both simulated and real dual-dose computed tomography perfusion image data and then compared with the state-of-the-art methods for brain dynamics computation by leveraging collaborations with Florida International University Herbert Wertheim College of Medicine, NewYork-Presbyterian Hospital / Weill Cornell Medical College (WCMC) and Northwell School of Medicine at Hofstra University. The proposed research will significantly advance the state-of-the-art in quantifying and analyzing brain structure and dynamics, and the interplay between the two for brain disease diagnosis, including both the acute and chronic diseases. This unified approach brings together fields of Computer Science, Bioengineering, Cognitive Neuroscience and Neuroradiology to create a framework for precisely measuring and analyzing the 5th dimension – brain dynamics – integrated with the 4-D brain with three dimensions from spatial data and one dimension from temporal data. Results from the project will be incorporated into graduate-level multi-disciplinary courses in machine learning, computational neuroscience and medical image analysis. This project will open up several new research directions in the domain of brain analysis, and will educate and nurture young researchers, advance the involvement of underrepresented minorities in computer science research, and equip them with new insights, models and tools for developing future research in brain dynamics in a minority serving university.

Education

  • Ph.D.

    Ph.D. in Electrical and Computer Engineering

    Cornell University, Ithaca, NY.

  • B.E.

    B.E. in Information Engineering

    Zhejiang University, Hangzhou, China

Academic Positions

  • BME

    Tenure-Track Assistant Professor

    University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering

  • IC3

    Associate Director

    University of Florida UF Intelligent Critical Care Center (IC3)

  • ECE

    Affliated Faculty

    University of Florida Department of Electrical and Computer Engineering

  • COM

    Affiliated Faculty

    University of Florida Department of Radiology, College of Medicine

  • CISE

    Affiliated Faculty

    University of Florida Department of Computer & Information Science & Engineering

  • CAM

    Affiliated Faculty

    University of Florida The Center for Cognitive Aging and Memory Clinical Translational Research (CAM)

Acknowledgements

  • Our work is supported by:

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Current Members

Ph.D. Students

Kyle See

BME Ph.D. Student

Biomedical Engineering

UF Graduate Student Preeminence Award
NIH CTSI TL1 Predoctoral Fellowship
kylebsee@ufl.edu

Skylar Stolte

BME Ph.D. Student

Biomedical Engineering

UF Graduate Student Preeminence Award
skylastolte4444@ufl.edu

Charlie Tran

ECE Ph.D. Student

Electrical and Computer Engineering

McNair Scholars Program
charlietran@ufl.edu

Seowung Leem

BME Ph.D. Student

Biomedical Engineering

leem.s@ufl.edu

Rachel Ho

APK Ph.D. Student

Applied Physiology and Kinesiology

NIH CTSI TL1 Predoctoral Fellowship
rachel.judy@ufl.edu
Co-advised with Dr. Steven Coombes via TL1 Program

M.S. Students

Jimmy Ossa

BME M.S. Student

Biomedical Engineering

runninggator@ufl.edu

Shen Kai

ECE M.S. Student

Electrical and Computer Engineering

shen.kai@ufl.edu

Jiaqing Zhang

ECE M.S. Student & Research Assistant

Electrical and Computer Engineering

jiaqing.zhang@ufl.edu

Research Assistants

Nathan Barkdull

Research Assistant

Physics and Math

nathan.barkdull@ufl.edu

Undergraduate Students

Garrett Fullerton

Undergraduate Student

Biomedical Engineering

NSF REU Awardee

University Scholar Program
gfullerton@ufl.edu

Gianna Sweeting

Undergraduate Student

Biomedical Engineering

Fernandez Family Scholar
University Scholar Program
sweetinggianna@ufl.edu

Simon Kato

Undergraduate Student

Math and Statistics

NSF REU Awardee

skato1@ufl.edu

Yiru Mu

Undergraduate Student

Biomedical Engineering

yiru.mu@ufl.edu

Maria Cardei

Undergraduate Student

Biomedical Engineering

mcardei@ufl.edu

Michael McGaha

Undergraduate Student

Computer Science and Statistics

michaelmcgaha@ufl.edu

Alvin Naiju

Undergraduate Student

Biochemistry

alvin.naiju@ufl.edu

Eugene Li

Undergraduate Student

Computer Science and Math

lieugene@ufl.edu

Jordi Bardia

Undergraduate Student

Computer Science

jbardia@ufl.edu

Keyur Patel

Undergraduate Student

Computer Science

kpatel6@ufl.edu

John Braddock

Undergraduate Student

Biomedical Engineering

bjohn.braddock@ufl.edu

Matthew Nguyen

Undergraduate Student

Biomedical Engineering

nguyen.matthew@ufl.edu

Alumni

Ph.D. Alumni

Yao Xiao

Doctor of Philosophy, 2017-2020

Biomedical Engineering

BMES 2020 Career Development Award
Graduate Student Speaker at College of Engineering Commencement 2020

Postdoc Fellow at MD Anderson

Peng Liu

Doctor of Philosophy, 2016-2021

Biomedical Engineering

Best Abstract Award at Montreal AI & Neuroscience (MAIN) Conference
UF Informatics Institute Graduate Fellowship
pliu1@ufl.edu

M.S. Alumni

Shreya Verma

Master of Engineering, BME, 2019-2021

Ph.D. student at Penn State University

Bhavin Soni

Master of Engineering, BME, 2019-2021

Neuroimaging Engineer, Washington University at St. Louis

Yangjunyi Li

Masters Student, UF BME, 2017-2018

Senior Scientist, Computational Biologist, Eli Lilly and Company

Yun Liang

UF BME, 2018-2019

PhD student, UF BME

Micheal Adeyosoye

MS-PhD, FIU Bridge to PhD Fellowship, 2016-2017

PhD Student, FIU

Jingan Qu

Masters Student, 2015-2017

Data Scientist, Applied Research Center FIU

Daniel Parra

Masters Student, 2015-2016

Software Engineer, ProActive Technologies, LLC

Haodi Jiang

Masters Student, 2014-2015

PhD student at NJIT

Sherman Ng

M.Eng. 2010-2011 @ Cornell ECE

Software Engineer, Microsoft


Undergraduate Alumni

Jason Chen

B.E. Computer Science, 2019-2021.

Software Development Engineer, Facebook

Maximillian Diaz

B.E. BME, 2018-2020
University Scholar Program
NSF Graduate Research Fellowship Program

Ph.D. student at UF

Daniel El Basha

B.E. BME, NSF REU Awardee 2017-2019
NSF Graduate Research Fellowship Program

PhD student at MD Anderson

Akshay Mathavan

Undergraduate Researcher, University Scholar Program, 2018-2019

Doctor of Medicine student, UF

Akash Mathavan

Undergraduate Researcher, University Scholar Program, 2018-2019

Doctor of Medicine student, UF


Visiting Students Alumni

Yuanyuan Zhu

Visiting Undergraduate Student, 2016 Summer

Masters Student, computer Science, University of South California

Xing Pang

Visiting Graduate Student, 2015-2016

Nanjing University of Science and Technology


K-12 Teachers

Edda Rivera

NSF DoD RET Teacher, Best NSF RET Poster Award at FIU SCIS RET Presentation, 2016 Summer

High School Teacher, John A. Ferguson Senior High School

Christian McDonald

NSF DoD RET Teacher, Best NSF RET Poster Award at FIU SCIS RET Presentation, 2016 Summer

High School Teacher, Miami Jackson Senior High School


K-12 Students

Paul Naghshineh

NSF DoD REU Student, 2016 Summer

Gaumard Scientific

Srividya Vaishnavi Surampudi

UF Student Science Training Program (SSTP), 2018 Summer

Stanford University

Imaan Randhawa

UF Student Science Training Program (SSTP), 2019 Summer

University of Florida

Aarushi Walia

UF Student Science Training Program (SSTP), 2019 Summer

University of Berkeley

Funded Projects

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    RF1AG071469: Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study

    NIH REPORT

    There is a pressing need for effective interventions to remediate age-related cognitive decline and alter the trajectory toward Alzheimer’s disease. The NIA Alzheimer’s Disease Initiative funded Phase III Augmenting Cognitive Training in Older Adults (ACT) trial aimed to demonstrate that transcranial direct current stimulation (tDCS) paired with cognitive training could achieve this goal. The present study proposes a state of the art secondary data analysis of ACT trial data that will further this aim by 1) elucidate mechanism of action underlying response to tDCS treatment with CT, 2) address heterogeneity of response in tDCS augmented CT by determining how individual variation in the dose of electrical current delivered to the brain interacts with individual brain anatomical characteristics; and 3) refine the intervention strategy of tDCS paired with CT by evaluating methods for precision delivery targeted dosing characteristics to facilitate tDCS augmented outcomes. tDCS intervention to date, including ACT, apply a fixed dosing approach whereby a single stimulation intensity (e.g., 2mA) and set of electrode positions on the scalp (e.g., F3/F4) is applied to all participants/patients. However, our recent work has demonstrated that age-related changes in neuroanatomy as well as individual variability in head/brain structures (e.g., skull thickness) significantly impacts the distribution and intensity of electrical current induced in the brain from tDCS. This project will use person-specific MRI-derived finite element computational models of electric current characteristics (current intensity and direction of current flow) and new methods for enhancing the precision and accuracy of derived models to precisely quantify the heterogeneity of current delivery in older adults. We will leverage these individualized precision models with state-of-the-art support vector machine learning methods to determine the relationship between current characteristics and treatment response to tDCS and CT. We will leverage the inherent heterogeneity of neuroanatomy and fixed current delivery to provide insight in the not only which dosing parameters were associated with treatment response, but also brain region specific information to facilitate targeted delivery of stimulation in future trials. Further still, the current study will also pioneer new methods for calculation of precision dosing parameters for tDCS delivery to potentially optimize treatment response, as well as identify clinical and demographic characteristics that are associated with response to tDCS and CT in older adults. Leveraging a robust and comprehensive behavioral and multimodal neuroimaging data set for ACT with advanced computational methods, the proposed study will provide critical information for mechanism, heterogeneity of treatment response and a pathway to refined precision dosing approaches for remediating age- related cognitive decline and altering the trajectory of older adults toward Alzheimer’s disease.

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    NSF IIS 2123809: Collaborative Research: SCH: Trustworthy and Explainable AI for Neurodegenerative Diseases

    NSF Abstract

    Driven by its performance accuracy, machine learning (ML) has been used extensively for various applications in the healthcare domain. Despite its promising performance, researchers and the public have grown alarmed by two unsettling deficiencies of these otherwise useful and powerful models. First, there is a lack of trustworthiness - ML models are prone to interference or deception and exhibit erratic behaviors when in action dealing with unseen data, despite good practice during the training phase. Second, there is a lack of interpretability - ML models have been described as 'black-boxes' because there is little explanation for why the models make the predictions they do. This has called into question the applicability of ML to decision-making in critical scenarios such as image-based disease diagnostics or medical treatment recommendation. The ultimate goal of this project is to develop computational foundation for trustworthy and explainable Artificial Intelligence (AI), and offer a low-cost and non-invasive ML-based approach to early diagnosis of neurodegenerative diseases. In particular, the project aims to develop computational theories, ML algorithms, and prototype systems. The project includes developing principled solutions to trustworthy ML and making the ML prediction process transparent to end-users. The later will focus on explaining how and why an ML model makes such a prediction, while dissecting its underlying structure for deeper understanding. The proposed models are further extended to a multi-modal and spatial-temporal framework, an important aspect of applying ML models to healthcare. A verification framework with end-users is defined, which will further enhance the trustworthiness of the prototype systems. This project will benefit a variety of high-impact AI-based applications in terms of their explainability, trustworthy, and verifiability. It not only advances the research fronts of deep learning and AI, but also supports transformations in diagnosing neurodegenerative diseases.

    This project will develop the computational foundation for trustworthy and explainable AI with several innovations. First, the project will systematically study the trustworthiness of ML systems. This will be measured by novel metrics such as, adversarial robustness and semantic saliency, and will be carried out to establish the theoretical basis and practical limits of trustworthiness of ML algorithms. Second, the project provides a paradigm shift for explainable AI, explaining how and why a ML model makes its prediction, moving away from ad-hoc explanations (i.e. what features are important to the prediction). A proof-based approach, which probes all the hidden layers of a given model to identify critical layers and neurons involved in a prediction from a local point of view, will be devised. Third, a verification framework, where users can verify the model's performance and explanations with proofs, will be designed to further enhance the trustworthiness of the system. Finally, the project also advances the frontier of neurodegenerative diseases early diagnosis from multimodal imaging and longitudinal data by: (i) identifying retinal vasculature biomarkers using proof-based probing in biomarker graph networks; (ii) connecting biomarkers of the retina and the brain vasculature via cross- modality explainable AI model; and, (iii) recognizing the longitudinal trajectory of vasculature biomarkers via a spatio-temporal recurrent explainable model. This synergistic effort between computer science and medicine will enable a wide range of applications to trustworthy and explainable AI for healthcare. The results of this project will be assimilated into the courses and summer programs that the research team have developed with specially designed projects to train students with trustworthy and explainable AI.

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    P01AI165380: Multi-Scale Evaluation and Mitigation of Toxicities Following Internal Radionuclide Contamination

    NIH REPORT

    History has taught us that exposures to radionuclides can happen any day almost anywhere in the US and elsewhere and we have done little to prepare ourselves. Our ability to perform dosimetry modeling for such scenarios and efforts into biomarker and mitigation discovery are archaic and our tendency to rely on external beam radiation to model these is utterly misplaced. We should and we can do much better. This program centers on the hypothesis that radiation from internal emitters is very unevenly distributed within a body, amongst organs, and even within organs, tissues and cells. The half-life and decay schema of the radionuclide, its activity and concentration, particle size and morphology, and its chemical form and solubility are all critical, as are the route of uptake, tissue structure, genetic makeup, physiology, danger signaling and the crosstalk with the immune system. Conceptually this suggests that the analysis of radionuclide distribution requires measurements at the MESO, MICRO and NANO level for accurate dosimetry modeling and biokinetics analyses, that will much better align with biological endpoints, and therefore with meaningful countermeasure development. In many ways our program integrates the three main pillars of radiation science, namely radiation physics, radiation chemistry and radiation biology, taking into account pharmacokinetics and pharmacodynamics aspects of particle distribution at subcellular, cellular, and tissue levels. In other words, to understand the biological effects of internal emitters and find the best possible mitigation strategies a systematic study is called for, one that includes but is not limited to: a) radionuclide physical and chemical form and intravital migration, b) protracted exposure times, c) radiation quality parameters, d) novel virtual phantom modeling beyond few MACRO reference models ; e) novel biokinetics with sex- and age- specificity; f) MESO, MICRO and NANO scale histology and immunohistochemistry with integrated radionuclide distribution information; g) exploration of molecular biomarkers of radionuclide intake and contamination and h) countermeasures that modulate radionuclide distribution and possibly also improve DNA, cell and tissue repair. We have assembled a team with diverse scientific expertise that can tackle these challenges within an integrated program. There is an incredibly impressive technological toolbox at our disposal and our goal is to generate a meaningful blueprint for understanding and predicting biological consequences of exposure to radionuclides. The possible benefits of this program to the radiation research community and the general population are immense.

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    U24AA029959: Southern HIV and Alcohol Research Consortium Biomedical Data Repository

    NIH REPORT

    The purpose of the SHARE P01 research program project is to address HIV and alcohol use around three themes; 1) Emerging adulthood (ages 18 -29); 2) Self-management of HIV and alcohol; and 3) Translational behavioral science. Emerging adulthood is a developmental stage marked by significant change in social roles, expectations as a new adult, and increased responsibilities. It is also marked by poor HIV self- management and increased alcohol use. Emerging adults with HIV (hereafter called young people living with HIV; YPLWH) may face even more challenges given intersectional stigma. This age group continues to have very high rates of new HIV infections. Interventions designed specifically for the unique developmental challenges of emerging adults are needed, yet emerging adults are often included with older adults in intervention programs. The concept of self-management emerged concurrently within both the substance abuse and chronic illness literatures, and fits well with the developmental challenges of emerging adulthood. Self-management, a framework we have utilized in our work with YPLWH, refers to the ability to manage symptoms, treatments, lifestyle changes, and consequences of health conditions. Current research now identifies individual-level self-management skills such as self-control, decision-making, self-reinforcement, and problem solving as that protect against substance use and improve other health outcomes and can be embedded in the Information-Motivation-Behavioral Skills model. Although we have conducted multiple studies with YPLWH, only one intervention to date (Healthy Choices conducted by our team) improved both alcohol use and viral suppression in YPLWH in large trials. The goal of the SHARE P01 is to utilize advances in translational behavioral science to optimize behavioral interventions and define new developmentally- and culturally-appropriate intervention targets to improve self-management of alcohol and HIV in YPLWH. We will focus our efforts in Florida, a state hardest hit by the HIV epidemic but with a particularly strong academic- community partnership to support translation. We have assembled research teams to conduct self- management studies across the translational spectrum to address self-management and improve alcohol use and viral suppression (and thereby reduce transmission) in diverse YPLWH in Florida. The P01 will consist of three research projects (DEFINE, ENGAGE, and SUSTAIN), representing different stages on the translational spectrum and targeting different core competencies, supported by two cores (Community Engagement Core and Data Science Core). If successful, the SHARE P01 has the potential to greatly advance programs promoting self-management of HIV and alcohol use among a particularly vulnerable, but under-researched group, emerging adults living with HIV. SHARE also has a high potential for scale-up and implementation beyond Florida and across the United States.

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    P01AA029543: Interventions to improve alcohol-related comorbidities along the gut-brain axis in persons with HIV infection

    NIH REPORT

    As persons living with HIV (PLWH) live longer, approximately 50% will experience HIV-related cognitive dysfunction, which may affect daily activities, contribute to morbidity and mortality, and increase the likelihood of HIV transmission. Alcohol consumption among PLWH may further exacerbate long-term cognitive dysfunction, with the presumed mechanism involving the gut microbiome, microbial translocation, systemic inflammation, and ultimately neuroinflammation. However, there are many gaps in our understanding regarding the specific pathophysiological mechanisms, and a need to offer interventions that are effective and acceptable in helping PLWH to reduce drinking or to protect them against alcohol-related harm. The overarching goal of this P01 is to identify and ultimately implement new/improved, targeted interventions that will improve outcomes related to cognitive and brain dysfunction in persons with HIV who drink alcohol. The proposed P01 activity will extend our current line of research that forms the core of the Southern HIV & Alcohol Research Consortium (SHARC). The specific aims of this P01 are to: 1) improve our understanding of the specific mechanisms that connect the gut microbiome to cognitive and brain health outcomes in persons with HIV; 2) evaluate interventions that are intended to reduce the impact of alcohol on brain and cognitive health in persons with HIV; and 3) connect and extend the research activity from this P01 with the training programs and community engagement activity in the SHARC. Our P01 will utilize two cores that provide infrastructure to two Research Components (RC1, RC2). The two RC will together enroll 200 PLWH with at-risk drinking into clinical trials that share common timepoints and outcome assessments. RC1 will compare two strategies to extend contingency management to 60 days, using breathalyzers and wrist-worn biosensors to monitor drinking. RC2 uses a hybrid trial design to evaluate two biomedical interventions targeting the gut-brain axis. One intervention is a wearable, transcutaneous vagus nerve stimulator that is hypothesized to stimulate the autonomic nervous system, resulting in decreased inflammation and improved cognition. The other intervention is a probiotic supplement intended to improve the gut microbiome in persons with HIV and alcohol consumption. All participants in RC2, and a subset of those in RC1 will have neuroimaging at two timepoints. The Data Science Core will provide data management and analytical support, and will analyze existing data and the data collected from this P01 using a machine learning and AI approach to identify factors associated with intervention success or failure. The Administrative Core will provide scientific leadership, clinical research and recruitment infrastructure, and connection to the outstanding training programs, development opportunities, and community engagement provided by the SHARC. Our community engagement with diverse populations, and collection of acceptability data from clinical trial participants, will facilitate our readiness to scale up the most promising interventions and move towards implementation in the next phase of our research.

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    P01AA029547: SHARE Program: Innovations in Translational Behavioral Science to Improve Self- management of HIV and Alcohol Reaching Emerging adults

    NIH REPORT

    The number of persons living with HIV (PLWH) continues to increase in the United States. Alcohol consumption is a significant barrier to both achieving the goal of ending the HIV epidemic and preventing comorbidities among PLWH, as it contributes to both HIV transmission and HIV-related complications. Recent advances in data capture systems such as mHealth devices, medical imaging, and high-throughput biotechnologies make large/complex research and clinical datasets available, including survey data, multi-omics data, electronic medical records, and/or other sources of reliable information related to engagement in care. This offers tremendous potential of applying “big” data to extract knowledge and insights regarding fundamental physiology, understand the mechanisms by which the pathogenic effects of biotic and abiotic factors are realized, and identify potential intervention targets. We propose to integrate the disparate data sources maintained by our partners and then utilize the big data to address research questions in treating HIV and alcohol-related morbidity and mortality. Specifically, we will pursue the following three aims: 1) Integrate the disparate data sources through standardization, harmonization, and merging; 2) Develop a web-based data sharing platform including virtual data sharing communities, data privacy protection, streamlined data approval and access, and tracking of ongoing research activities; 3) Provide statistical support to junior investigators to use the data repository for exploratory data analysis and proposal development. The proposed study will tap into disparate data sources, unleash the potential of data and information, accelerate knowledge discovery, advance data-powered health, and transform discovery to improve health outcomes for PLWH.

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    R01MH125615: Acquisition, extinction, and recall of attention biases to threat: Computational modeling and multimodal brain imaging

    NIH REPORT

    Classical aversive conditioning is a well-established laboratory model for studying acquisition and extinction of defensive responses. In experimental animals, as well as in humans, research to date has been mainly focused on the role of limbic structures (e.g., the amygdala) in these responses. Recent evidence has begun to stress the important contribution by the brain’s sensory and attention control systems in maintaining the neural representations of conditioned responses and in facilitating their extinction. The proposed research breaks new ground by combining novel neuroimaging techniques with advanced computational methods to examine the brain’s visual and attention processes underlying fear acquisition and extinction in humans. Major advances will be made along three specific aims. In Aim 1, we characterize the brain network dynamics of visuocortical threat bias formation, extinction, and recall in a two-day learning paradigm. In Aim 2, we establish and test a computational model of threat bias generalization. In Aim 3, we examine the relation between individual differences in generalization and recall of conditioned visuocortical threat biases and individual differences in heightened autonomic reactivity to conditioned threat, a potential biomarker for assessing the predisposition to developing the disorders of fear and anxiety. It is expected that accomplishing these research aims will address two NIMH strategic priorities: defining the circuitry and brain networks underlying complex behaviors (Objective 1) and identifying and validating new targets for treatment that are derived from the understanding of disease mechanisms (Objective 3). It is further expected that this project will enable a paradigm shift in research on dysfunctional attention to threat from one that focuses primarily on limbic-prefrontal circuits to one that emphasizes the interactions among sensory, attention, executive control and limbic systems.

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    U01NS119562: Web-based Automated Imaging Differentiation of Parkinsonism

    NIH REPORT

    Across the globe, there has been a considerable growth in the number of people diagnosed with Parkinsonism. Estimates indicate that from 1990 to 2015 the number of Parkinsonism diagnoses doubled, with more than 6 million people currently carrying the diagnosis, and by year 2040, 12 and 14.2 million people will be diagnosed with Parkinsonism. Parkinson’s disease (PD), multiple system atrophy Parkinsonian variant (MSAp), and progressive supranuclear palsy (PSP) are neurodegenerative forms of Parkinsonism, which can be difficult to diagnose as they share similar motor and non-motor features, and they each have an increased chance of developing dementia. In the first five years of a PD diagnosis, about 58% of PD are misdiagnosed, and of these misdiagnoses about half have either MSA or PSP. Since PD, MSAp, and PSP require unique treatment plans and different medications, and clinical trials testing new medications require the correct diagnosis, there is an urgent need for both clinic ready and clinical-trial ready markers for differential diagnosis of PD, MSAp, and PSP. Over the past decade, we have developed diffusion imaging as an innovative biomarker for differentiating PD, MSAp, and PSP. In this proposal, we will leverage our extensive experience to create a web-based software tool that can process diffusion imaging data from anywhere in the world. We will disseminate and test the tool in the largest prospective cohort of participants with Parkinsonism (PD, MSAp, PSP), working closely with the Parkinson Study Group. The reason to test this in the Parkinson Study Group network, is because they are the community that evaluates Phase II and Phase III clinical trials in Parkinsonism. This web-based software tool will be capable of reading raw diffusion imaging data, performing quality assurance procedures, analyzing the data using a validated pipeline, and providing imaging metrics and diagnostic probability. We will test the performance of the wAID-P by enrolling 315 total subjects (105 PD, 105 MSAp, 105 PSP) across 21 sites in the Parkinson Study Group. Each site will perform imaging, clinical scales, diagnosis, and will upload the data to the web-based software tool. The clinical diagnosis will be blinded to the diagnostic algorithm and the imaging diagnosis will be compared to the movement disorders trained neurologist diagnosis. We will also enroll a portion of the cohort into a brain bank to ascertain pathological confirmation and to test the algorithm against cases with post-mortem diagnoses. The final outcome will be to disseminate a validated diagnostic algorithm to the Parkinson neurological and radiological community and to make it available to all on a website.

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    IIS-1908299 III: Small: Collaborative Research: Modeling Multi-Level Connectivity of Brain Dynamics

    Project Page

    The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer's disease. Existing efforts at characterizing brain dynamics have predominantly centered on 'isolated' models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.

  • IIS-1564892 CRII: SCH: Characterizing, Modeling and Evaluating Brain Dynamics

    The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer's disease. Existing efforts at characterizing brain dynamics have predominantly centered on 'isolated' models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.

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    CNS-1747783: Phase I IUCRC University of Florida: Center for Big Learning

    This project establishes the NSF Industry/University Collaborative Research Center for Big Learning (CBL). The vision is to create intelligence towards intelligence-driven society. Through catalyzing the fusion of diverse expertise from the consortium of faculty members, students, industry partners, and federal agencies, CBL seeks to create state-of-the-art deep learning methodologies and technologies and enable intelligent applications, transforming broad domains, such as business, healthcare, Internet-of-Things, and cybersecurity. This timely initiative creates a unique platform for empowering our next-generation talents with cutting-edge technologies of societal relevance and significance. This project establishes the NSF Industry/University Collaborative Research Center for Big Learning (CBL) at University of Florida (UF). With substantial breakthroughs in multiple modalities of challenges, such as computer vision, speech recognition, and natural language understanding, the renaissance of machine intelligence is dawning. The CBL vision is to create intelligence towards intelligence-driven society. The mission is to pioneer novel deep learning algorithms, systems, and applications through unified and coordinated efforts in the CBL consortium. The UF Site will focus on intelligent platforms and applications and closely collaborate with other sites on deep learning algorithms, systems, and applications. The CBL will have broad transformative impacts in technologies, education, and society. CBL aims to create pioneering research and applications to address a broad spectrum of real-world challenges, making significant contributions and impacts to the deep learning community. The discoveries from CBL will make significant contributions to promote products and services of industry in general and CBL industry partners in particular. As the magnet of deep learning research and applications, CBL offers an ideal platform to nurture next-generation talents through world-class mentors from both academia and industry, disseminates the cutting-edge technologies, and facilitates industry/university collaboration and technology transfer. The center repository will be hosted at http://nsfcbl.org. The data, code, documents will be well organized and maintained on the CBL servers for the duration of the center for more than five years and beyond. The internal code repository will be managed by GitLab. After the software packages are well documented and tested, they will be released and managed by popular public code hosting services, such as GitHub and Bitbucket.

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    Artificial Intelligence Catalyst Award: VCA-DNN: Neuroscience-Inspired Artificial Intelligence for Visual Emotion Recognition

    Human emotions are dynamic, multidimensional responses to challenges and opportunities, which emerge from network interactions in the brain. Disruptions of these network interactions underlie emotional dysregulation in many mental disorders, including anxiety and depression. Creating an AI-based model system that is informed and validated by known biological findings and can be used to carry out causal manipulations and test the consequences against human imaging data will thus be a highly significant development in the short term. The long-term goal is to understand how the human brain processes emotional information and how the process breaks down in mental disorders. NIH currently funds the team to record and analyze fMRI data from humans viewing natural images of varying emotional content. In the process of their research, they recognize that empirical studies such as theirs have significant limitations. Chief among them is the lack of ability to manipulate the system to establish the causal basis for the observed relationship between brain and behavior. The advent of AI, especially deep neural networks (DNNs), opens a new avenue to address this problem. Creating an AI-based model system that is informed and validated by known biological findings and that can be used to carry out causal manipulations and allow the testing of the consequences against human imaging data will thus be a significant step toward achieving our long-term goal.

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    UFII: Biology and Cognition Inspired Deep Learning

    Human emotions are dynamic, multidimensional responses to challenges and opportunities, which emerge from network interactions in the brain. Disruptions of these network interactions underlie emotional dysregulation in many mental disorders, including anxiety and depression. Creating an AI-based model system that is informed and validated by known biological findings and can be used to carry out causal manipulations and test the consequences against human imaging data will thus be a highly significant development in the short term. The long-term goal is to understand how the human brain processes emotional information and how the process breaks down in mental disorders. NIH currently funds the team to record and analyze fMRI data from humans viewing natural images of varying emotional content. In the process of their research, they recognize that empirical studies such as theirs have significant limitations. Chief among them is the lack of ability to manipulate the system to establish the causal basis for the observed relationship between brain and behavior. The advent of AI, especially deep neural networks (DNNs), opens a new avenue to address this problem. Creating an AI-based model system that is informed and validated by known biological findings and that can be used to carry out causal manipulations and allow the testing of the consequences against human imaging data will thus be a significant step toward achieving our long-term goal.

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    UFII Junior SEED Award: Multimodal Visual-Text Learning from Clinical Narrative and Image for Early Detection of Diabetic Retinopathy

    Vision-threatening diseases are one of the leading causes of blindness. DR, a common complication of diabetes, is the leading cause of blindness in American adults and the fastest growing disease threatening nearly 415 million diabetic patients worldwide. With professional eye imaging devices such as fundus cameras or Optical Coherence Tomography (OCT) scanners, most of the vision-threatening diseases can be curable if detected early. However, these diseases are still damaging people’s vision and leading to irreversible blindness, especially in rural areas and low-income communities where professional imaging devices and medical specialists are not available or not even affordable. There is an urgent need for early detection of vision-threatening diseases before vision loss in these areas. The current practice of DR screening relies on human experts to manually examine and diagnose DR in stereoscopic color fundus photographs at hospitals using professional fundus camera, which is time-consuming and infeasible for large-scale screening. It also puts an enormous burden on ophthalmologists and increases waiting lists and may undermine the standards of health care. Therein, automatic DR diagnosis systems with ophthalmologist-level performance are a critical and unmet need for DR screening. Electronic Health Records (EHR) have been increasingly implemented in US hospitals. Vast amounts of longitudinal patient data have been accumulated and are available electronically in structured tables, narrative text, and images. There is an increasing need for multimodal synergistic learning methods to link different data sources for clinical and translational studies. Recent emerging of AI technologies, especially deep learning (DL) algorithms, have greatly improved the performance of automated vision-disease diagnosis systems based on EHR data. However, the current systems are unable to detect early stage of vision-diseases. On the other hand, the clinical text provides detailed diagnosis, symptoms, and other critical observations documented by physicians, which could be a valuable resource to help lesion detection from medical images. Multimodal synergistic learning is the key to linking clinical text to medical images for lesion detection. This study proposes to leverage the narrative clinical text to improve lesion level detection from medical images via clinical Natural Language Processing (NLP). The team hypothesizes that early stage vision-threatening diseases can be detected using smartphone-based fundus camera via multimodal learning integrating clinical text and images with limited lesion-level labels via clinical NLP. The ultimate goal is to improve the early detection and prevention of vision-threatening diseases among rural and low-income areas by developing a low-cost, highly efficient system that can leverage both clinical narratives and images.

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    UFII-CTSI Pilot Award: Toward prevention of cardiotoxicity in cancer: a multimodal approach leveraging genomics, images and clinical data

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    NIH CTSI TL1: Predicting short-term and long-term effects of spinal cord stimulation: implications for clinical practice

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    Pilot Award: Precision Dose:Personalized Radiation Dose Optimization for Multimodal Imaging

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    Modeling, Estimating and Reasoning in Limited Data Brain

    In the current era of invigorating brain research, there is emerging attention in leveraging machine learning in understanding the brain, particularly exploring the brain dynamics. With the ever-increasing amount of neuroscience data, new challenges and opportunities arise for brain dynamics analysis, such as data-driven reconstruction and computer-aided diagnosis. However, there are few attempts to bridge the semantic gaps between the raw brain imaging data and the diagnosis. We will develop robust and data-driven techniques for the purpose of modeling, estimating functional parameters from the limited data brain images, and making decision support practical based on efficient direct estimation of the brain dynamics. This is an interdisciplinary research combining medical image analysis, machine learning, neuroscience, and the domain expertise.

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PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration

Journal
Peng Liu, Linsong Xu, Garrett Fullerton, Yao Xiao, James-Bond Nguyen, Zhongyu Li, Izabella Barreto, Catherine Olguin, Ruogu Fang
Frontiers in Radiology
Publication Year: May, 2022

Abstract

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.

DOMINO: Domain-aware Model Calibration in Medical Image Segmentation

Early Acceptance (rate=13%)
Conference
Skylar Stolte, Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam Woods, Kevin Brink, Matthew Hale, Ruogu Fang
25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Singapore.
Publication Year: September 18-22, 2022

Unraveling Somatotopic Organization in the Human Brain using Machine Learning and Adaptive Supervoxel-based Parcellations

Journal
Kyle B. See, David J. Arpin, David E. Vaillancourt, Ruogu Fang, Stephen A. Coombes
NeuroImage
Publication Date: November, 2021

Abstract

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy? To answer these questions, we developed a novel experimental approach. fMRI data were collected while human subjects performed a precisely controlled force generation task separately with their hand, foot, and mouth. We used a simple linear iterative clustering (SLIC) algorithm to segment whole-brain beta coefficient maps to build an adaptive brain parcellation and then classified effectors using extreme gradient boosting (XGBoost) based on parcellations at various spatial resolutions. This allowed us to understand how data-driven adaptive brain parcellation granularity altered classification accuracy. Results revealed effector-dependent activity in regions of the post-central gyrus, precentral gyrus, and paracentral lobule. SMA, regions of the inferior and superior parietal lobule, and cerebellum each contained effector-dependent and effector-independent representations. Machine learning analyses showed that increasing the spatial resolution of the data-driven model increased classification accuracy, which reached 94% with over 1,755 supervoxels. Our SLIC-based supervoxel parcellation outperformed classification analyses using established brain templates and random simulations. Occlusion experiments further demonstrated redundancy across the sensorimotor network when classifying effectors. Our observations extend our understanding of effector-dependent and effector-independent organization within the human brain and provide new insight into the functional neuroanatomy required to predict the motor effector used in a motor control task.

Baseline neuroimaging predicts decline to dementia from amnestic mild cognitive impairment

Journal
Joseph M Gullett, Alejandro Albizu, Ruogu Fang, David A Loewenstein, Ranjan Duara, Monica Rosselli, Melissa J Armstrong, Tatjana Rundek, Hanna K Hausman, Steven T Dekosky, Adam J Woods, Ronald A Cohen
Frontiers in Aging Neuroscience
Publication Date: November, 2021

Abstract

Background and Objectives: Prediction of decline to dementia using objective biomarkers in high-risk patients with amnestic mild cognitive impairment (aMCI) has immense utility. Our objective was to use multimodal MRI to 1) determine whether accurate and precise prediction of dementia conversion could be achieved using baseline data alone, and 2) generate a map of the brain regions implicated in longitudinal decline to dementia.

Methods: Participants meeting criteria for aMCI at baseline (N=55) were classified at follow-up as remaining stable/improved in their diagnosis (N=41) or declined to dementia (N=14). Baseline T1 structural MRI and resting-state fMRI (rsfMRI) were combined and a semi-supervised support vector machine (SVM) which separated stable participants from those who decline at follow-up with maximal margin. Cross-validated model performance metrics and MRI feature weights were calculated to include the strength of each brain voxel in its ability to distinguish the two groups.

Results: Total model accuracy for predicting diagnostic change at follow-up was 92.7% using baseline T1 imaging alone, 83.5% using rsfMRI alone, and 94.5% when combining T1 and rsfMRI modalities. Feature weights that survived the p<0.01 threshold for separation of the two groups revealed the strongest margin in the combined structural and functional regions underlying the medial temporal lobes in the limbic system.

Discussion: An MRI-driven SVM model demonstrates accurate and precise prediction of later dementia conversion in aMCI patients. The multi-modal regions driving this prediction were the strongest in the medial temporal regions of the limbic system, consistent with literature on the progression of Alzheimer’s disease.

CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation

Journal
Peng Liu, Charlie Tran, Bin Kong, Ruogu Fang
Neurocomputing
Publication Date: October 29, 2021

Abstract

Recently, deep neural networks have demonstrated comparable and even better performance than board-certified ophthalmologists in well-annotated datasets. However, the diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models trained on one domain to new testing domains. In this paper, we propose a multi-scale input along with multiple domain adaptors applied hierarchically in both feature and output spaces. The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge. Multi-scale inputs can reduce the information loss due to the pooling layers used in the network for feature extraction, while our proposed CADA is an interactive paradigm that presents an exquisite collaborative adaptation through both adversarial learning and ensembling weights at different network layers. In particular, to produce a better prediction for the unlabeled target domain data, we simultaneously achieve domain invariance and model generalizability via adversarial learning at multi-scale outputs from different levels of network layers and maintaining an exponential moving average (EMA) of the historical weights during training. Without annotating any sample from the target domain, multiple adversarial losses in encoder and decoder layers guide the extraction of domain-invariant features to confuse the domain classifier. Meanwhile, the ensembling of weights via EMA reduces the uncertainty of adapting multiple discriminator learning. Comprehensive experimental results demonstrate that our CADA model incorporating multi-scale input training can overcome performance degradation and outperform state-of-the-art domain adaptation methods in segmenting retinal optic disc and cup from fundus images stemming from the REFUGE, Drishti-GS, and Rim-One-r3 datasets. The code will be available at https://github.com/cswin/CADA

Rotational and Reflectional Equivariant Convolutional Neural Network for data-limited applications: Multiphase Flow demonstration

Journal
Bhargav Siddani, S. Balachandar, Ruogu Fang
Physics of Fluids
Publication Date: October 22, 2021

Abstract

This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles in a dispersed multiphase setup using convolutional neural network (CNN). The considered problem involves rotational symmetry about the mean velocity (streamwise) direction. Thus, this work enforces this symmetry using SE(3)-equivariant, special Euclidean group of dimension 3, CNN architecture, which is translation and three-dimensional rotation equivariant. This study mainly explores the generalization capabilities and benefits of a SE(3)-equivariant network. Accurate synthetic flow fields for Reynolds number and particle volume fraction combinations spanning over a range of [86.22, 172.96] and [0.11, 0.45], respectively, are produced with careful application of symmetry-aware data-driven approach.

Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks

Journal
Bhargav Siddani, S. Balachandar., William C. Moore, Yunchao Yang, Ruogu Fang
Theoretical and Computational Fluid Dynamics
Publication Date: October 28, 2021

Abstract

Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper we present a machine learning methodology using Generative Adversarial Network framework and Convolutional Neural Network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and par- ticle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45] respectively. Test performance of the model for the studied cases is very promising.
Keywords: Pseudo-turbulence, Multiphase Flow prediction, Generative Adversarial Network (GAN), Convolutional Neural Network (CNN)

A deep neural network for emotion perception

Best Abstract Award (Undegraduate and Graduate Level)
Conference
Peng Liu, Ke Bo, Lihan Cui, Yujun Chen, Charlie Tran, Mingzhou Ding†, Ruogu Fang†,
(† co-corresponding authors)
Montreal AI & Neuroscience Annual Meeting
November 29-30, Montreal, Canada
Publication Date: November 29-30, 2021

Emergence of emotion selectivity in deep neural networks trained to recognize visual objects

Conference
Peng Liu, Ke Bo, Lihan Cui, Yujun Chen, Charlie Tran, Mingzhou Ding†, Ruogu Fang†,
(† co-corresponding authors)
Society of Neuroscience (SfN) Annual Meeting
Virtual: November 8-11, In-Person: November 13-16, Chicago, IL
Publication Date: November 8-16, 2021

A deep neural network model for emotion perception

Conference
Peng Liu, Ke Bo, Lihan Cui, Yujun Chen, Charlie Tran, Ruogu Fang†, Mingzhou Ding†
(† co-corresponding authors)
Society of Neuroscience (SfN) Annual Meeting
Virtual: November 8-11, In-Person: November 13-16, Chicago, IL
Publication Date: November 8-16, 2021

Machine-Learning Defined Precision tDCS for Improving Cognitive Function

Conference
Alejandro Albizu, Ruogu Fang, Aprinda Indahlastari, Andrew O’Shea, Skylar E. Stolte, Kyle B. See, Emanuel M. Boutzoukas, Jessica N. Kraft, Nicole R. Nissim, and Adam J. Woods
Society of Neuroscience (SfN) Annual Meeting
Virtual: November 8-11, In-Person: November 13-16, Chicago, IL
Publication Date: November 8-16, 2021

Identify Diabetic Retinopathy-related Clinical Concepts Using Transformer-based Natural Language Processing Methods

Conference
Zehao Yu, Xi Yang, Gianna L Sweeting, Yinghan Ma, Skylar E Stolte, Ruogu Fang, Yonghui Wu
The Fourth International Workshop on Health Natural Language Processing, Victoria, Canada (virtual)
Publication Date: August 9, 2021

MAGIC: Multitask Automated Generation of Inter-modal CT Perfusion Maps via Generative Adversarial Network

Conference
Garrett Fullerton, Simon Kato, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Orlando, FL.
Publication Date: October 6-9, 2021

Predicting Treatment Outcome in Spinal Cord Stimulation with EEG

Conference
Kyle See, Rachel Judy, Stephen Coombes, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Orlando, FL.
Publication Date: October 6-9, 2021

Ensemble Machine Learning for Alzheimer’s disease Classification from Retinal Vasculature

Conference
Hely Lin, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Orlando, FL.
Publication Date: October 6-9, 2021

TL1 Team Approach to Predicting Response to Spinal Cord Stimulation for Chronic Low Back Pain

Conference
Kyle See, Rachel Ho, Steven Coombes, Ruogu Fang
Journal of Clinical and Translational Sciences
Publication Date: March 30, 2021

Modular machine learning for Alzheimer's disease classification from retinal vasculature

Journal
Jianqiao Tian, Glenn Smith, Han Guo, Boya Liu, Zehua Pan, Zijie Wang, Shuangyu Xiong, Ruogu Fang
Nature Scientific Reports
Publication Date: January 8, 2021

Abstract

Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.

Machine learning and individual variability in electric field characteristics predict tDCS treatment response.

Journal
Alejandro Albizu, Ruogu Fang, Aprinda Indahlastari, Andrew OShea, Skylar E. Stolte, Kyle B. See, Emanuel M. Boutzoukas, Jessica N. Kraft, Nicole R. Nissim and Adam J. Woods
Brain Stimulation
Publication Year: November/December, 2020

Abstract

Background

Transcranial direct current stimulation (tDCS) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that electric current delivery to the brain can vary significantly across individuals. Quantification of this variability could enable person-specific optimization of tDCS outcomes. This pilot study used machine learning and MRI-derived electric field models to predict working memory improvements as a proof of concept for precision cognitive intervention.

Methods

Fourteen healthy older adults received 20 minutes of 2 mA tDCS stimulation (F3/F4) during a two-week cognitive training intervention. Participants performed an N-back working memory task pre-/post-intervention. MRI-derived current models were passed through a linear Support Vector Machine (SVM) learning algorithm to characterize crucial tDCS current components (intensity and direction) that induced working memory improvements in tDCS responders versus non-responders.

Main results

SVM models of tDCS current components had 86% overall accuracy in classifying treatment responders vs. non-responders, with current intensity producing the best overall model differentiating changes in working memory performance. Median current intensity and direction in brain regions near the electrodes were positively related to intervention responses.

Conclusions

This study provides the first evidence that pattern recognition analyses of MRI-derived tDCS current models can provide individual prognostic classification of tDCS treatment response with 86% accuracy. Individual differences in current intensity and direction play important roles in determining treatment response to tDCS. These findings provide important insights into mechanisms of tDCS response as well as proof of concept for future precision dosing models of tDCS intervention.

Machine Learning for Parkinsons Disease Diagnosis Using Fundus Eye Images

Featured by Forbes and 30+ Media
Conference
Maximillian Diaz, Jianqiao Tian, Adolfo Ramirez-Zamora, Ruogu Fang
Annual Meeting of Radiology Society of North America
Publication Year: December, 2020

Artificial Intelligence For Characterizing Heart Failure In Cardiac Magnetic Resonance Images

Conference
Skylar Stolte, Yonghui Wu, William R Hogan, Yan Gong, Ruogu Fang
American Heart Association Scientific Session
Publication Year: November 13-17, 2020

Physiological wound assessment from coregistered and segmented tissue hemoglobin maps

Journal
E. A. Robledo, R. Schutzman, R. Fang, C. Fernandez, R. Kwasinski, K. Leiva, F. Perez-Clavijo, and A. Godavarty
Journal of the Optical Society of America A
Publication Year: July, 2020

Abstract

A handheld near-infrared optical scanner (NIROS) was recently developed to map for effective changes in oxy- and deoxyhemoglobin concentration in diabetic foot ulcers (DFUs) across weeks of treatment. Herein, a coregistration and image segmentation approach was implemented to overlay hemoglobin maps onto the white light images of ulcers. Validation studies demonstrated over 97% accuracy in coregistration. Coregistration was further applied to a healing DFU across weeks of healing. The potential to predict changes in wound healing was observed when comparing the coregistered and segmented hemoglobin concentration area maps to the visual area of the wound.

TL1 Team Approach to Predicting Short-term and Long-term Effects of Spinal Cord Stimulation

Journal
Kyle See, Rachel Louise Mahealani Judy, Stephen Coombes, Ruogu Fang
Journal of Clinical and Translational Science
Publication Year: July, 2020

Abstract

OBJECTIVES/GOALS: Spinal cord stimulation (SCS) is an intervention for patients with chronic back pain. Technological advances have led to renewed optimism in the field, but mechanisms of action in the brain remain poorly understood. We hypothesize that SCS outcomes will be associated with changes in neural oscillations. METHODS/STUDY POPULATION: The goal of our team project is to test patients who receive SCS at 3 times points: baseline, at day 7 during the trial period, and day 180 after a permanent system has been implanted. At each time point participants will complete 10 minutes of eyes closed, resting electroencephalography (EEG). EEG will be collected with the ActiveTwo system, a 128-electrode cap, and a 256 channel AD box from BioSemi. Traditional machine learning methods such as support vector machine and more complex models including deep learning will be used to generate interpretable features within resting EEG signals. RESULTS/ANTICIPATED RESULTS: Through machine learning, we anticipate that SCS will have a significant effect on resting alpha and beta power in sensorimotor cortex. DISCUSSION/SIGNIFICANCE OF IMPACT: This collaborative project will further the application of machine learning in cognitive neuroscience and allow us to better understand how therapies for chronic pain alter resting brain activity.

Deep Learning For Multimodal CT Image Quality Enhancement and Radiation Exposure Optimization

PhD Thesis
Yao Xiao
Biomedical Engineering, University of Florida
Publication Year: 2020

A Survey on Medical Image Analysis in Diabetic Retinopathy

Journal
Skylar Stolte, Ruogu Fang
Medical Image Analysis, In Press
Publication Year: May 30, 2020

Abstract

Diabetic Retinopathy (DR) represents a highly-prevalent complication of diabetes in which individuals suffer from damage to the blood vessels in the retina. The disease manifests itself through lesion presence, starting with microaneurysms, at the nonproliferative stage before being characterized by neovascularization in the proliferative stage. Retinal specialists strive to detect DR early so that the disease can be treated before substantial, irreversible vision loss occurs. The level of DR severity indicates the extent of treatment necessary - vision loss may be preventable by effective diabetes management in mild (early) stages, rather than subjecting the patient to invasive laser surgery. Using artificial intelligence (AI), highly accurate and efficient systems can be developed to help assist medical professionals in screening and diagnosing DR earlier and without the full resources that are available in specialty clinics. In particular, deep learning facilitates diagnosis earlier and with higher sensitivity and specificity. Such systems make decisions based on minimally handcrafted features and pave the way for personalized therapies. Thus, this survey provides a comprehensive description of the current technology used in each step of DR diagnosis. First, it begins with an introduction to the disease and the current technologies and resources available in this space. It proceeds to discuss the frameworks that different teams have used to detect and classify DR. Ultimately, we conclude that deep learning systems offer revolutionary potential to DR identification and prevention of vision loss.

Determinants of Treatment Response to Transcranial Direct Current Stimulation

Outstanding Presentation by Early Career Scientist Award
Presentation
Albizu A, Fang R, Indahlastari A, Nissim NR, OShea A, Woods AJ
5th Annual NYC Neuromodulation Conference
Publication Year: April 20-22, 2020

Building Personalized Medicine Models for Therapeutic Applications of Transcranial Electrical Stimulation

Conference
Albizu A, Indahlastari A, Nissim NR, OShea A, Ruogu Fang, Woods AJ
48th Annual Meeting of the International Neuropsychological Society
Publication Year: February 2020

Domain-Invariant Interpretable Fundus Image Quality Assessment

Journal
Yaxin Shen, Bin Sheng, Ruogu Fang, Huating Li, Ling Dai, Skylar Stolte*, Jing Qin, Weiping Jia, Dinggang Shen
Medical Image Analysis, volume 61
Publication Year: 2020

Abstract

Objective and quantitative assessment of fundus image quality is essential for the diagnosis of retinal diseases. The major factors in fundus image quality assessment are image artifact, clarity, and field definition. Unfortunately, most of existing quality assessment methods focus on the quality of overall image, without interpretable quality feedback for real-time adjustment. Furthermore, these models are often sensitive to the specific imaging devices, and cannot generalize well under different imaging conditions. This paper presents a new multi-task domain adaptation framework to automatically assess fundus image quality. The proposed framework provides interpretable quality assessment with both quantitative scores and quality visualization for potential real-time image recapture with proper adjustment. In particular, the present approach can detect optic disc and fovea structures as landmarks, to assist the assessment through coarse-to-fine feature encoding. The framework also exploit semi-tied adversarial discriminative domain adaptation to make the model generalizable across different data sources. Experimental results demonstrated that the proposed algorithm outperforms different state-of-the-art approaches and achieves an area under the ROC curve of 0.9455 for the overall quality classification.

Multi-Series CT Image Super-Resolution by using Transfer Generative Adversarial Network

Conference
Yao Xiao, Manuel M. Arreola, Izabella Barreto, Wesley E. Bolch, W. Christopher Fox, Keith Peters, Dhanashree A. Rajderkar, John H. Rees, Ruogu Fang
Society for Imaing Informatics in Medicine Annual Meeting in Austin, TX
Publication Year: June 24-26, 2020

Transfer-GAN: Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Networks

Conference
Yao Xiao, Keith R. Peters, W. Christopher Fox, John H. Rees, Dhanashree A. Rajderkar, Manuel M. Arreola, Izabella Barreto, Wesley E. Bolch, Ruogu Fang
IEEE International Symposium on Biomedical Imaging in Iowa City, IA
Publication Year: April 3-7, 2020

Transfer Generative Adversarial Network for Multimodal CT Image Super-Resolution

Conference
Yao Xiao, Ruogu Fang
SPIE Medical Imaging in Houston, TX
Publication Year: February 15-20, 2020

Monte Carlo Dosimetry For CT Brain Perfusion Studies Utilizing Volumetric Acquisitions

Conference
Justin L Brown, Daniel El Basha∗, Nathalie Correa, Yao Xiao, Izabella Barreto, Ruogu Fang, Chan Kim, Wesley E. Bolch
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo
Publication Year: 2020

ZMAT4 and DOCK9 Variants Associated with Heart Failure in Breast Cancer Patients in the UK Biobank data

Presidential Trainee Award, 2020 David J. Goldstein Trainee Award
Conference
Marwa Tantawy, Sonal Singh, Guang Yang, Matt Gitzendanner, Yiqing Chen, Yonghui Wu, Ruogu Fang, William Hogan, Yan Gong
American Society for Clinical Pharmacology and Therapeutics Annual Meeting in Houston, TX
Publication Year: March 18-21, 2020

Low-Rise Gable Roof Buildings Pressure Prediction using Deep Neural Networks

Journal
Jianqiao Tian, Kurtis Gurley, Maximillian Diaz, Pedro L. Fernández Cabán, Forrest J. Masters, Ruogu Fang
Journal of Wind Engineering & Industrial Aerodynamics
Publication Year: 2020

Abstract

This paper presents a deep neural network (DNN) based approach for predicting mean and peak wind pressure coefficients on the surface of a scale model low-rise, gable roof residential building. Pressure data were collected on the model at multiple prescribed wind directions and terrain roughnesses. The resultant pressure coefficients quantified from a subset of these directions and terrains were used to train an DNN to predict coefficients for directions and terrains excluded from the training. The approach is able to leverage a variety of input conditions to predict pressure coefficients with high accuracy, while the prior work has limited flexibility with the number of input variables and yielded lower prediction accuracy. A two-step nested DNN procedure is introduced to improve the prediction of peak coefficients. The optimal correlation coefficients of return predictions were 0.9993 and 0.9964, for mean and peak coefficient prediction, respectively. The concept of super resolution based on global prediction was also discussed. With a sufficiently large database, the proposed DNN-based approach can augment existing experimental methods to improve the yield of knowledge while reducing the number of tests required to gain that knowledge.

IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

Journal
Prasanna Porwal, Samiksha Pachade, Manesh Kokare, Girish Deshmukh, Jaemin Son, Woong Bae, Lihong Liu, Jianzong Wang, Xinhui Liu, Liangxin Gao, TianBo Wu, Jing Xiao, Fengyan Wang, Baocai Yin, Yunzhi Wang, Gopichandh Danala, Linsheng He, Yoon Ho Choi, Yeong Chan Lee, Sang-Hyuk Jung, Zhongyu Li, Xiaodan Sui, Junyan Wu, Xiaolong Li, Ting Zhou, Janos Toth, Agnes Baran, Avinash Kori, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Xingzheng Lyu,r, Li Cheng,D, Qinhao Chu, Pengcheng Li, Xin Ji, Sanyuan Zhang, Yaxin Shen, Ling Dai$, Oindrila Saha, Rachana Sathish, Tânia Melo, Teresa Araújo, Balazs Harangi, Bin Sheng, Ruogu Fang, Debdoot Sheet, Andras Hajdu, Yuanjie Zheng, Ana Maria Mendonça, Shaoting Zhang, Aurélio Campilho, Bin Zheng, Dinggang Shen, Luca Giancardo, Gwenolé Quellec, Fabrice Mériaudeau
Medical Image Analysis, volume 59
Publication Year: 2020

Abstract

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on “Diabetic Retinopathy – Segmentation and Grading” was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.

REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs

Journal
Jose Ignacio Orlando, Huazhu Fu, Joa ̃o Barbossa Breda, Karel van Keer, Deepti R. Bathula, Andre ́s Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, JoonHo Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton van den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Xiulan Zhang, Yanwu Xu, Hrvoje Bogunovic ́
Medical Image Analysis, volume 59
Publication Year: 2020

Abstract

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MICCAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.

Deep Spatial-Temporal Convolutional Neural Networks for Medical Image Restoration Deep Learning and Convolutional Neural Networks for Medical Image Computing

Book Chapter
Yao Xiao, Skylar Stolte, Peng Liu, Yun Liang, Pina Sanelli, Ajay Gupta, Jana Ivanidze, Ruogu Fang
Deep Learning and Convolutional Neural Networks for Medical Image Computing, Springer Publisher
Publication Year: 2019

Abstract

Computed tomography perfusion (CTP) facilitates low-cost diagnosis and treatment of acute stroke. Cine scanning allows users to visualize brain anatomy and blood flow in virtually live time. However, effective visualization exposes patients to radiocontrast pharmaceuticals and extended scan times. Higher radiation dosage exposes patients to potential risks including hair loss, cataract formation, and cancer. To alleviate these risks, radiation dosage can be reduced along with tube current and/or X-ray radiation exposure time. However, resulting images may lack sufficient information or be affected by noise and/or artifacts. In this chapter, we propose a deep spatial-temporal convolutional neural network to preserve CTP image quality at reduced tube current, low spatial resolution, and shorter exposure time. This network structure extracts multi-directional features from low-dose and low-resolution patches at different cross sections of the spatial-temporal data and reconstructs high-quality CT volumes. We assess the performance of the network concerning image restoration at different tube currents and multiple resolution scales. The results indicate the ability of our network in restoring high-quality scans from data captured at as low as 21% of the standard radiation dose. The proposed network achieves an average improvement of 7% in perfusion maps compared to the state-of-the-art method.

Big Data in Computational Health Informatics

Book Chapter
Ruogu Fang, Samira Pouyanfar, Yimin Yang, Yao Xiao, Jianqiao Tian, Shu-Ching Chen, and S.S. Iyengar
Big Data in Multimodal Medical Imaging, CRC Publisher
Publication Year: 2019

Abstract

The recent explosive surge of digital health data has made a significant impact on the proliferation of data science research in healthcare. However, traditional approaches have reached limited achievements in facing the big health data due to their weak scalability and poor applicability in tackling massive amount of healthcare data. By the structured analysis of the historical and modern methods, this article presents a comprehensive overview of the existing challenges, techniques, and future directions of computational health informatics in the big data era. This chapter outlines the challenges in the generic big health data as four high Vs, which are high volume, velocity, variety, and veracity. Moreover, it introduces a systematic data processing pipeline that covers data capturing, storing, sharing, analyzing, searching, and decision support. In this book chapter, we compare and categorize numerous machine learning techniques and algorithms for computational health informatics, based on which, we identify and analyze the essential prospects lying ahead in this big data age.

Development and Validation of the Automated Imaging Differentiation in Parkinsonism (AID-P): A Multi-Site Machine Learning Study

Journal
Derek B. Archer, ..., Ruogu Fang, ..., David E. Vaillancourt
The Lancet Digital Health
Publication Year: 2019

Identifying Relations of Medications with Adverse Drug Events Using Recurrent Convolutional Neural Networks and Gradient Boosting

Journal
Xi Yang, Jiang Bian, Ruogu Fang, Ragnhildur I. Bjarnadottir, William R. Hogan, and Yonghui Wu
Journal of the American Medical Informatics Association
Publication Year: 2019

Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising

Journal
Peng Liu, Mohammad D El Basha, Yangjunyi Li, Yao Xiao, Pina C.Sanelli, Ruogu Fang
Medical Image Analysis, vol. 54, pp. 306-315
Publication Year: 2019

Abstract

Deep convolutional neural networks offer state-of-the-art performance for medical image analysis. However, their architectures are manually designed for particular problems. On the one hand, a manual designing process requires many trials to tune a large number of hyperparameters and is thus quite a time-consuming task. On the other hand, the fittest hyperparameters that can adapt to source data properties (e.g., sparsity, noisy features) are not able to be quickly identified for target data properties. For instance, the realistic noise in medical images is usually mixed and complicated, and sometimes unknown, leading to challenges in applying existing methods directly and creating effective denoising neural networks easily. In this paper, we present a Genetic Algorithm (GA)-based network evolution approach to search for the fittest genes to optimize network structures automatically. We expedite the evolutionary process through an experience-based greedy exploration strategy and transfer learning. Our evolutionary algorithm procedure has flexibility, which allows taking advantage of current state-of-the-art modules (e.g., residual blocks) to search for promising neural networks. We evaluate our framework on a classic medical image analysis task: denoising. The experimental results on computed tomography perfusion (CTP) image denoising demonstrate the capability of the method to select the fittest genes for building high-performance networks, named EvoNets. Our results outperform state-of-the-art methods consistently at various noise levels.

STIR-Net: Spatial-Temporal Image Restoration Net for CT Perfusion Radiation Reduction

Journal
Yao Xiao, Peng Liu, Yun Liang, Skylar Stolte, Pina Sanelli, Ajay Gupta, Jana Ivanidze, Ruogu Fang
Frontiers in Nuerology
Publication Year: 2019

Abstract

Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT slices effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales.The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method.

Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning

Journal
Saleha Masood, Ruogu Fang, Huating Li, Bin Sheng, Ping Li, Akash Mathavan, Xiangning Wang, Po Yang, Qiang Wu, Jing Qin, and Weiping Jia
Nature Scientific Reports, 9(1):3058
Publication Year: 2019

Abstract

The choroid layer is a vascular layer in human retina and its main function is to provide oxygen and support to the retina. Various studies have shown that the thickness of the choroid layer is correlated with the diagnosis of several ophthalmic diseases. For example, diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. Despite contemporary advances, automatic segmentation of the choroid layer remains a challenging task due to low contrast, inhomogeneous intensity, inconsistent texture and ambiguous boundaries between the choroid and sclera in Optical Coherence Tomography (OCT) images. The majority of currently implemented methods manually or semi-automatically segment out the region of interest. While many fully automatic methods exist in the context of choroid layer segmentation, more effective and accurate automatic methods are required in order to employ these methods in the clinical sector. This paper proposed and implemented an automatic method for choroid layer segmentation in OCT images using deep learning and a series of morphological operations. The aim of this research was to segment out Bruch’s Membrane (BM) and choroid layer to calculate the thickness map. BM was segmented using a series of morphological operations, whereas the choroid layer was segmented using a deep learning approach as more image statistics were required to segment accurately. Several evaluation metrics were used to test and compare the proposed method against other existing methodologies. Experimental results showed that the proposed method greatly reduced the error rate when compared with the other state-of-the-art methods.

Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Network

Conference
Yao Xiao, Manual Arreola, Izabella Barrreto, W. Christopher Fox, Keith Peters, Ruogu Fang
Annual Meeting of Radiology Society of North American
Publication Year: 2019

CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation

Conference
Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, Ruogu Fang
Medical Image Analysis and Computer Assisted Intervention
Publication Year: 2019

Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning

Conference
Siyuan Pan, Yuxin Xue, Bin Sheng, Xuhong Hou, Huating Li, Ruogu Fang, Weiping Jia, and Jing Qin
Medical Image Analysis and Computer Assisted Intervention
Publication Year: 2019

Multimodal CT Image Super-Resolution via Transfer-GAN.

Conference
Yao Xiao, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Philadelphia, PA
Publication Year: 2019

Deep Learning-based Alzheimers Disease Classification of FDG-PET and AV45 PET Images.

Conference
Jianqiao Tian, Max Diaz, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Philadelphia, PA
Publication Year: 2019

Artificial Intelligence For Automated Diagnosis of Glaucoma In Stereoscopic Images

Conference
Skylar Stolte, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Philadelphia, PA
Publication Year: 2019

Classification Of Neural Stimulations In The Brain With Super Voxels

Conference
Kyle See, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Philadelphia, PA
Publication Year: 2019

Retinal Disease Diagnosis Using Mobile Devices

Conference
Skylar Stolte, Kyle See, Daniel El Basha, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Philadelphia, PA
Publication Year: 2019

Mining Big Neuron Morphological Data

Journal
Maryamossadat Aghili, Ruogu Fang
Computational Intelligence and Neuroscience
Publication Year: 2018

Abstract

The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.

Retinal Vessel Segmentation Using Minimum Spanning Superpixel Tree Detector

Journal
Bin Sheng, Ping Li, Shuangjia Mo, Huating Li, Xuhong Hou, Qiang Wu, Jing Qin, Ruogu Fang, and David Dagan Feng
IEEE Transaction on Cybernetics, 99, pp.1-13
Publication Year: 2018

Abstract

The retinal vessel is one of the determining factors in an ophthalmic examination. Automatic extraction of retinal vessels from low-quality retinal images still remains a challenging problem. In this paper, we propose a robust and effective approach that qualitatively improves the detection of low-contrast and narrow vessels. Rather than using the pixel grid, we use a superpixel as the elementary unit of our vessel segmentation scheme. We regularize this scheme by combining the geometrical structure, texture, color, and space information in the superpixel graph. And the segmentation results are then refined by employing the efficient minimum spanning superpixel tree to detect and capture both global and local structure of the retinal images. Such an effective and structure-aware tree detector significantly improves the detection around the pathologic area. Experimental results have shown that the proposed technique achieves advantageous connectivity-area-length (CAL) scores of 80.92% and 69.06% on two public datasets, namely, DRIVE and STARE, thereby outperforming state-of-the-art segmentation methods. In addition, the tests on the challenging retinal image database have further demonstrated the effectiveness of our method. Our approach achieves satisfactory segmentation performance in comparison with state-of-the-art methods. Our technique provides an automated method for effectively extracting the vessel from fundus images

Neural Network Evolution Using Expedited Genetic Algorithm for Medical Image Denoising

Conference
Peng Liu, Yangjunyi Li, Mohammad D El Basha, Ruogu Fang
Medical Image Analysis and Computer Assisted Intervention, Granada, Spain.
Publication Year: 2018

Abstract

Convolutional neural networks offer state-of-the-art performance for medical image denoising. However, their architectures are manually designed for different noise types. The realistic noise in medical images is usually mixed and complicated, and sometimes unknown, leading to challenges in creating effective denoising neural networks. In this paper, we present a Genetic Algorithm (GA)-based network evolution approach to search for the fittest genes to optimize network structures. We expedite the evolutionary process through an experience-based greedy exploration strategy and transfer learning. The experimental results on computed tomography perfusion (CTP) images denoising demonstrate the capability of the method to select the fittest genes for building high-performance networks, named EvoNets, and our results compare favorably with state-of-the-art methods

Multi-task Fundus Image Quality Assessment via Transfer Learning and Landmarks Detection

Conference
Yaxin Shen, Ruogu Fang, Bin Sheng, Ling Dai, Huating Li, Jing Qin, Qiang Wu, and Weiping Jia
Machine Learning in Medical Imaging, Granada, Spain.
Publication Year: 2018

Abstract

The quality of fundus images is critical for diabetic retinopathy diagnosis. The evaluation of fundus image quality can be affected by several factors, including image artifact, clarity, and field definition. In this paper, we propose a multi-task deep learning framework for automated assessment of fundus image quality. The network can classify whether an image is gradable, together with interpretable information about quality factors. The proposed method uses images in both rectangular and polar coordinates, and fine-tunes the network from trained model grading of diabetic retinopathy. The detection of optic disk and fovea assists learning the field definition task through coarse-to-fine feature encoding. The experimental results demonstrate that our framework outperform single-task convolutional neural networks and reject ungradable images in automated diabetic retinopathy diagnostic systems.

Low-Dose CT Perfusion Image Restoration and Radiation Reduction.

Conference
Yao Xiao, Peng Liu, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Atlanta, GA
Publication Year: 2018

Efficient Multi Modality Medical Image Joint Recosntruction via Vectorized Gradient

Conference
Yao Xiao, Yun Liang, Yunmei Chen, Xiaojing Ye, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Atlanta, GA
Publication Year: 2018

Multi-Modality Brain Image Co-Registration

Conference
Skylar Stolte, Yao Xiao, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Atlanta, GA
Publication Year: 2018

Decision Tree-based Classification for Differentiating System Lupus Erythematosus and Mixed Connective Tissue Disease

Conference
Kyle B. See, Ruogu Fang
Biomedical Engineering Society Annual Meeting, Atlanta, GA
Publication Year: 2018

STDN: Spatial-Temporal Denoising Net for Radiation Optimization in CT Perfusion

Conference
Yao Xiao, Liupeng, Yun Liang, Ruogu Fang
ACM Richard Tapia Celebration of Diversity in Computing, September, Orlando, FL
Publication Year: 2018

Multi-Modality PET-MRI Image Joint Reconstruction

Conference
Yao Xiao, Yun Liang, Yunmei Chen, Xiaojing Ye, Ruogu Fang
ASNR 56th Annual Meeting & The Foundation of the ASNR Symposium, Vancouver, Canada
Publication Year: 2018

Image Super-Resolution and Radiation Reduction via Deep Learning

Conference
Yao Xiao, Pina C. Sanelli, Ruogu Fang
ASNR 56th Annual Meeting & The Foundation of the ASNR Symposium, Vancouver, Canada
Publication Year: 2018

Regulated-convolutional Networks for Low-dose Cerebral CT Perfusion Restoration

Conference
Peng Liu, Ruogu Fang
ASNR 56th Annual Meeting & The Foundation of the ASNR Symposium, Vancouver, Canada
Publication Year: 2018

SDCNET: Smoothed Dense-Convolution Network For Restoring Low-Dose Cerebral CT Perfusion

Conference
Peng Liu, Ruogu Fang
IEEE International Symposium on Biomedical Imaging, Washington D.C.
Publication Year: 2018

Neural Network Evolution Using Expedited Genetic Algorithm for Medical Image Denoising

Patents
Peng Liu, Ruogu Fang
Ref. No: UF-17344, Filed on 9/10/2018, Provisional Patent. US 62/728,995
Publication Year: 2018

Automatic Choroid Layer Segmentation Using Normalized Graph Cut

Journal
Saleha Masood, Bin Sheng, Ping Li, Ruimin Shen, Ruogu Fang, Qiang Wu
IET Image Processing. pp. 22, DOI:10.1049/iet-ipr.2017.0273, Online ISSN 1751-9667, 2017.
Publication Year: 2017

Abstract

Optical coherence tomography (OCT) is an immersive technique for depth analysis of retinal layers. Automatic choroid layer segmentation is a challenging task because of the low contrast inputs. Existing methodologies carried choroid layer segmentation manually or semi-automatically. In this paper, we proposed automated choroid layer segmentation based on normalized cut algorithm, which aims at extracting the global impression of images and treats the segmentation as a graph partitioning problem. Due to the complexity of the layering structure of retinal layers and choroid layer, we employed a series of preprocessing to make the cut more deterministic and accurate. The proposed method divided the image into several patches and ran the normalized cut on every image patch separately. The aim was to avoid insignificant vertical cuts and focus on horizontal cutting. After processing every patch, we acquired a global cut on the original image by combining all the patches. Later we measured the choroidal thickness which is highly helpful in the diagnosis of several retinal diseases. The results were computed on a total of 525 images of 21 real patients. Experimental results showed that the mean relative error rate of the proposed method was around 0.4 as the compared the manual segmentation performed by the experts.

STAR: Spatio-Temporal Architecture for super-Resolution in Low-Dose CT Perfusion.

Conference
Yao Xiao, Ajay Gupta, Pina C. Sanelli, Ruogu Fang
Machine Learning in Medical Imaging (MIML), Medical Image Computing and Computer Assisted Intervention (MICCAI), Lecture Notes in Computer Science book series (LNCS, volume 10541), pp 97-105, Quebec, Canada, Sep. 2017.
Publication Year: 2017

Abstract

Computed tomography perfusion (CTP) is one of the most widely used imaging modality for cerebrovascular disease diagnosis and treatment, especially in emergency situations. While cerebral CTP is ca- pable of quantifying the blood flow dynamics by continuous scanning at a focused region of the brain, the associated excessive radiation increases the patients' risk levels of developing cancer. To reduce the necessary radiation dose in CTP, decreasing the temporal sampling frequency is one promising direction. In this paper, we propose STAR, an end-to- end Spatio-Temporal Architecture for super-Resolution to significantly reduce the necessary scanning time and therefore radiation exposure. The inputs into STAR are multi-directional 2D low-resolution spatio- temporal patches at different cross sections over space and time. Via training multiple direction networks followed by a conjoint reconstruc- tion network, our approach can produce high-resolution spatio-temporal volumes. The experiment results demonstrate the capability of STAR to maintain the image quality and accuracy of cerebral hemodynamic parameters at only one-third of the original scanning time.

Cardiovascular Disease Prediction and Risk Factor Mining with RFMiner.

Conference
Yao Xiao, Ruogu Fang
Biomedical Engineering Society Annual Meeting (BMES), 2017 Annual Meeting, Phoenix, Arizona.
Publication Year: 2017

Accelerated Brain Perfusion Imaging via Spatio-Temporal Super-Resolution.

Conference
Yao Xiao, Ruogu Fang
Biomedical Engineering Society Annual Meeting (BMES), 2017 Annual Meeting, Phoenix, Arizona.
Publication Year: 2017

A Simple and Realistic Simulation Method for Low-Dose CT.

Conference
Peng Liu, Ruogu Fang>
Biomedical Engineering Society Annual Meeting (BMES), 2017 Annual Meeting, Phoenix, Arizona.
Publication Year: 2017

RFMiner: Risk Factors Discovery and Mining for Preventive Cardiovascular Health.

Conference
Yao Xiao, Ruogu Fang
The Second IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Philadelphia, PA
Publication Year: 2017

Abstract

Cardiovascular disease is one of the leading causes of death in the United States. It is critical to identify the risk factors associated with cardiovascular diseases and to alert individuals before they experience a heart attack. In this paper we propose RFMiner, a risk factor discovery and mining framework for identifying significant risk factors using integrated measures. We provide the blueprints for accurately predicting the possibility of heart attacks in the near future while identifying notable risk factors - especially the factors which are not well recognized.

Indexing and Mining Large-Scale Neuron Databases using Maximum Inner Product Search.

Journal
Zhongyu Li, Ruogu Fang, Fumin Shen, Amin Katouzian, Shaoting Zhang.
Pattern Recognition, vol. 63, pp. 680-688
Publication Year: 2017

Abstract

Morphological retrieval is an effective approach to explore large-scale neuronal databases, as the morphology is correlated with neuronal types, regions, functions, etc. In this paper, we focus on the neuron identification and analysis via morphological retrieval. In our proposed framework, multiple features are extracted to represent 3D neuron data. Because each feature reflects different levels of similarity between neurons, we group features into different hierarchies to compute the similarity matrix. Then, compact binary codes are generated from hierarchical features for efficient similarity search. Since neuronal cells usually have tree-topology structure, it is hard to distinguish different types of neurons simply via traditional binary coding or hashing methods based on Euclidean distance metric and/or linear hyperplanes. Therefore, we employ an asymmetric binary coding strategy based on the maximum inner product search (MIPS), which not only makes it easier to learn the binary coding functions, but also preserves the non-linear characteristics of the neuron morphological data. We evaluate the proposed method on more than 17,000 neurons, by validating the retrieved neurons with associated cell types and brain regions. Experimental results show the superiority of our approach in neuron morphological retrieval compared with other state-of-the-art methods. Moreover, we demonstrate its potential use cases in the identification and analysis of neuron characteristics from large neuron databases.

Abdominal Adipose Tissues Extraction Using Multi-Scale Deep Neural Network.

Journal
Fei Jiang, Huating Li, Xuhong Hou, Bin Sheng, Ruimin Shen, Xiao-Yang Liu, Weiping Jia, Ping Li, Ruogu Fang
NeuroComputing, vol. 229, pp. 23-33
Publication Year: 2017

Abstract

Segmentation of abdominal adipose tissues (AAT) into subcutaneous adipose tissues (SAT) and visceral adipose tissues (VAT) is of crucial interest for managing the obesity. Previous methods with raw or hand-crafted features rarely work well on large-scale subject cohorts, because of the inhomogeneous image intensities, artifacts and the diverse distributions of VAT. In this paper, we propose a novel two-stage coarse-to-fine algorithm for AAT seg- mentation. In the first stage, we formulate the AAT segmentation task as a pixel-wise classification problem. First, three types of features, intensity, spatial and contextual fea- tures, are adopted. Second, a new type of deep neural network, named multi-scale deep neural network (MSDNN), is provided to extract high-level features. In the second stage, to improve the segmentation accuracy, we refine coarse segmentation results by determining the internal boundary of SAT based on coarse segmentation results and continuous of SAT internal boundary. Finally, we demonstrate the efficacy of our algorithm for both 2D and 3D cases on a wide population range. Compared with other algorithms, our method is not only more suitable for large-scale dataset, but also achieves better segmentation results. Fur- thermore, our system takes about 2 seconds to segment an abdominal image, which implies potential clinical applications.

Towards High-Throughput Abnormal Brain Screening in MRI Images

Conference
Maryamossadat Aghili, Ruogu Fang
Women in Machine Learning Workshop, Neural Information Processing Systems (NIPS), Barcelona, Spain.
Publication Year: 2016

TENDER: TEnsor Non-local Deconvolution Enabled Radiation Reduction in CT Perfusion.

Journal
Ruogu Fang, Ajay Gupta, Junzhou Huang, Pina Sanelli.
NeuroComputing, vol. 229, pp. 13-22
Publication Year: 2016

Abstract

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. CT remains one of the most widely used imaging modality for stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and longterm health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization to integrate contextual and non-local information. We also propose an efficient framework consisting of fast nearest neighbor search, accelerated optimization and parallel computing to improve the efficiency and scalability of the non-local spatio-temporal algorithm. Evaluations on clinical data of subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

Computational Health Informatics in the Big Data Age: A Survey

Journal
Ruogu Fang*, Samira Pouyanfar*, Yimin Yang, Shu-Ching Chen, S. S. Iyengar (* indicates equal contributions)
Journal CSUR, ACM Computing Survey, Volume 49 Issue 1, Article No. 12
Publication Year: 2016

Abstract

The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods. We have summarized the challenges into four Vs (i.e., volume, velocity, variety, and veracity) and proposed a systematic data-processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.

Hemodynamic Imaging of Lower Extremity Ulcers.

Conference
Rebecca Kwasinski, Cristianne Fernandez, Kevin Leiva, Edwin Robledo, Yuanyuan Zhu, Penelope Kallis, Francesco-Perez Clavijo, Ruogu Fang, Robert Kirsner, Anuradha Godavarty.
Innovations in Wound Healing
Publication Year: 2016

Abstract

Clinicians employ visual inspection of the wound and reduction in its size over time to monitor its healing process. Although these are standard clinical assessments, there is a need to develop a physiological approach that can map sub-surface tissue oxygenation at and around the wound region. Recently, a non-contact, portable, hand-held near infrared optical scanner (NIROS) has been developed to functionally image wound sites and differentiate healing from non-healing in lower extremity ulcers. Past studies using NIROS focused on differentiating healing from non-healing wounds based on NIR optical contrast between the wound and healthy surrounding tissue. However, these studies did not showcase the physiological changes in tissue oxygenation. Herein, NIROS has been modified to perform multi wavelength imaging in order to obtain the oxy and deoxy- hemoglobin maps of the wound and its surroundings. Clinical studies are currently performed at two clinical sites in Miami on lower extremity ulcers (2, diabetic foot ulcers (DFUs) and 4 venous leg ulcers (VLUs to date). Preliminary results have shown changes in oxy- and deoxy-hemoglobin maps of the wound and background across weeks of the treatment process. Image segmentation studies quantified regions of varied tissue oxygenation around and beneath the wound to potentially determine sub-surface healing regions. Ongoing efforts involve systematic 8-week imaging studies to obtain physiological indicators of healing from hemodynamic studies of DFUs and VLUs.

CT perfusion image super-resolution using a deep convolutional network.

Conference
Paul Naghshineh, Peng Liu, Ruogu Fang
BMES, Biomedical Engineering Society Annual Meeting in Minneapolis, Minnesota.
Publication Year: 2016

Physiological Assessment of Wound Healing using a Near-Infrared Optical Scanner.

Conference
Anuradha Godavarty, Rebecca Kwasinki, Cristianne Fernandez, Yuanyuan Zhu, Edwin Robledo, F. Perez-Clavijo, Ruogu Fang
Biomedical Engineering Society Annual Meeting (BMES) in Minneapolis, Minnesota.
Publication Year: 2016

Maximum Inner Product Search for Morphological Retrieval of Large-Scale Neuron Data

Conference
Zhongyu Li, Fumin Shen, Ruogu Fang, Sailesh Conjeti, Amin Katouzian, Shaoting Zhang.
ISBI, IEEE International Symposium on Biomedical Imaging, Prague, Czech Republic
Publication Year: 2016

Abstract

Morphological retrieval is an effective approach to explore neurons' databases, as the morphology is correlated with neuronal types, regions, functions, etc. In this paper, we focus on the neuron identification and analysis via morphological retrieval. In our proposed framework, both global and local features are extracted to represent 3D neuron data. Then, compacted binary codes are generated from original features for efficient similarity search. As neuron cells usually have tree-topology structure, it is hard to distinguish different types of neuron simply via traditional binary coding or hashing methods based on Euclidean distance metric and/or linear hyperplanes. Thus, we propose a novel binary coding method based on the maximum inner product search (MIPS), which is not only more easier to learn the binary coding function, but also preserves the non-linear characteristics of neuron morphology data. We evaluate the proposed method on more than 17,000 neurons, by validating the retrieved neurons with associated cell types and brain regions. Experimental results show the superiority of our approach in neuron morphological retrieval compared with other state-of-the-art methods. Moreover, we demonstrate its potential use case in the identification and analysis of neuron characteristics.

Direct Estimation of Permeability Maps for Low-Dose CT Perfusion

Conference
Ruogu Fang, Ajay Gupta, Pina C. Sanelli
ISBI, IEEE International Symposium on Biomedical Imaging, Prague, Czech Republic
Publication Year: 2016

Abstract

With the goal of achieving low radiation exposure from medical imaging, computed tomography perfusion (CTP) introduces challenging problems for both image reconstruction and perfusion parameter estimation in the qualitative and quantitative analyses. Conventional approaches address the reconstruction and the estimation processes separately. Since the hemodynamic parameter maps have much lower dimensionality than the original sinogram data, estimating hemodynamic parameters directly from sinogram will further reduce radiation exposure and save computational resources to reconstruct the intermediate time-series images. In this work, we propose the first direct estimation framework for CTP that integrates the time-series image reconstruction, contrast conversion, hematocrit correction and hemodynamic parameter estimation in one optimization function, which is solved using an efficient algorithm. Evaluations on the digital brain perfusion phantom and a clinical acute stroke subject demonstrate that the proposed direct estimation framework boosts the estimation accuracy remarkably in CTP scanning with lower radiation exposure.

Automatic Segmentation of Lower Extremity Ulcers in Near-Infrared Optical Imaging

Conference
Ruogu Fang, Xing Pang, Arash Dadkhah, Jiali Lei, Elizabeth Solis, Suset Rodriguesz, Francisco Perez-Clavijo, Stephen Wigley, Charles Buscemi, Anuradha Godvarty.
ISBI, IEEE International Symposium on Biomedical Imaging, Prague, Czech Republic
Publication Year: 2016

Near-Infrared Optical Imaging and Wound Segmentation in Lower Extremity Ulcers

Conference
Xing Pang, Arash Dadkhah, Jaili Lei, Elizabeth Solis, Suset Rodriguez, Francisco Perez-Clavijo, Stephen Wigley, Ruogu Fang, Anuradha Godvarty.
OSA, Optical Society of America Annual Meeting
Publication Year: 2016

Abstract

Near-Infrared (NIR) optical imaging can reveal tissue oxygenation of the wound, complementing the visual inspection of the surface granulation. Herein, graph cuts algorithm is applied to segment NIR images of the wound from its peripheries.

Wound Size Measurement of Lower Extremity Ulcers Using Segmentation Algorithms

Conference
Arash Dadkhah, Xing Pang, Elizabeth Solis, Ruogu Fang, Anuradha Godvarty.
SPIE Proceedings in Photonics West, San Francisco
Publication Year: 2016

Abstract

Lower extremity ulcers are one of the most common complications that not only affect many people around the world but also have huge impact on economy since a large amount of resources are spent for treatment and prevention of the diseases. Clinical studies have shown that reduction in the wound size of 40% within 4 weeks is an acceptable progress in the healing process. Quantification of the wound size plays a crucial role in assessing the extent of healing and determining the treatment process. To date, wound healing is visually inspected and the wound size is measured from surface images. The extent of wound healing internally may vary from the surface. A near-infrared (NIR) optical imaging approach has been developed for non-contact imaging of wounds internally and differentiating healing from non-healing wounds. Herein, quantitative wound size measurements from NIR and white light images are estimated using a graph cuts and region growing image segmentation algorithms. The extent of the wound healing from NIR imaging of lower extremity ulcers in diabetic subjects are quantified and compared across NIR and white light images. NIR imaging and wound size measurements can play a significant role in potentially predicting the extent of internal healing, thus allowing better treatment plans when implemented for periodic imaging in future.

Efficient 4D Non-Local Tensor Total-Variation for Low-Dose CT Perfusion Deconvolution

Journal
Ruogu Fang, Ming Ni, Junzhou Huang, Qianmu Li, Tao Li
Medical Computer Vision: Algorithms for Big DataChapter, no.16, Lecture Notes in Computer Science
Publication Year: 2015

Abstract

Tensor total variation deconvolution has been recently proposed as a robust framework to accurately estimate the hemodynamic parameters in low-dose CT perfusion by fusing the local anatomicalstructure correlation and temporal blood flow continuation. However the locality property in the current framework constrains the search for anatomical structure similarities to the local neighborhood, missing the global and long-range correlations in the whole anatomical structure. This limitation has led to noticeable absence or artifact of delicate structures, including the critical indicators for the clinical diagnosis of cerebrovascular diseases. In this paper, we propose an extension of the TTV framework by introducing 4D non-local tensor total variation into the deconvolution to bridge the gap between non-adjacent regions of the same tissue classes. The non-local regularization using tensor total variation term is imposed on the spatio-temporal flow-scaled residue functions. An efficient algorithm and implementation of the non-local tensor total variation (NL-TTV) reduces the time complexity with fast similarity computation, accelerated optimization and parallel operations. Extensive evaluations on the clinical data with cerebrovascular diseases and normal subjects demonstrate the importance of non-local linkage and long-range connections for low-dose CT perfusion deconvolution.

Robust Low-dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Journal
Ruogu Fang, Shaoting Zhang, Tsuhan Chen, Pina C. Sanelli.
TMI, IEEE Transaction on Medical Imaging, vol.34, no.7, pp.1533-1548
Publication Year: 2015

Abstract

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain' is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in-vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects overestimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

Guest Editorial: Sparsity Techniques in Medical Imaging.

Journal
Ruogu Fang, Tsuhan Chen, Dimitris Metaxas, Pina Sanelli, Shaoting Zhang.
CMIG, Elsevier Journal of Computerized Medical Imaging and Graphics, vol. 46, no. 1
Publication Year: 2015

Abstract

With the advent of the age for big data and complex structure, sparsity has been an important modeling tool in compressed sensing, machine learning, image processing, neuroscience and statistics. In the medical imaging field, sparsity methods have been successfully used in image reconstruction, image enhancement, image segmentation, anomaly detection, disease classification, and image database retrieval. Developing more powerful sparsity models for a large range of medical imaging and medical image analysis problems as well as efficient optimization and learning algorithm will keep being a main research topic in this field. The goal of this special issue is to publish original and high quality papers on innovation research and development in medical imaging and medical image analysis using sparsity techniques. This special issue will help advance the scientific research within the field of sparsity methods for medical imaging.

Tissue-Specific Sparse Deconvolution for Brain CT Perfusion.

Journal
Ruogu Fang,Haodi Jiang, Junzhou Huang.
CMIG, Elsevier Journal of Computerized Medical Imaging and Graphics, vol.46, no.1, pp. 64-72
Publication Year: 2015

Abstract

Enhancing perfusion maps in low-dose computed tomography perfusion (CTP) for cerebrovascular disease diagnosis is a challenging task, especially for lowcontrast tissue categories where infarct core and ischemic penumbra usually occur. Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the lowcontrast tissue classes where infarct core and ischemic penumbra are likely to occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we propose a tissue-specific sparse deconvolution approach to preserve the subtle perfusion information in the low-contrast tissue classes. We first build tissue-specific dictionaries from segmentations of high-dose perfusion maps using online dictionary learning, and then perform deconvolution-based hemodynamic parameters estimation for block-wise tissue segments on the low-dose CTP data. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method compared to state-of-art, and potentially improve diagnostic accuracy by increasing the differentiation between normal and ischemic tissues in the brain

Wound Segmentation in Near-Infrared Optical Imaging

Conference
Ruogu Fang, Xing Pang, Arash Dadkhah, Jiali Lei, Elizabeth SOlis, Suset ROdriguez, Francisco Perez-Calvijo, Stephen Wigley, Charles Buscemi, Anuradha Godvarty.
Innovation in Wound Healing, Hawks Cay, FL
Publication Year: 2015

Fast Preconditioning for Accelerated Multi-Contrast MRI Reconstruction

Conference
Ruoyu Li, Yeqing Li, Ruogu Fang,Shaoting Zhang, Hao Pan, Junzhou Huang
MICCAI'15, In Proc. of the 18th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Munich, Germany
Publication Year: 2015

Efficient 4D Non-Local Tensor Total-Variation for Low-Dose CT Perfusion Deconvolution

Conference
Ruogu Fang, Ming Ni, Junzhou Huang, Qianmu Li, and Tao Li
The 18th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Workshop on Medical Computer Vision, Munich, Germany
Publication Year: 2015

Robust Low-Dose CT Perfusion Deconvolution via Non-Local Tensor Total Variation

Conference
Ruogu Fang, Ming Ni, Junzhou Huang, Qianmu Li, Tao Li
BMES, Biomedical Engineering Society Annual Meeting, Tampa, FL
Publication Year: 2015

Introduction

Stroke and cerebrovascular diseases are the leading cause of serious, long-term disability in the United States. Computed tomography perfusion (CTP) is one of the most widely accepted imaging modality for stroke care. However, the high radiation exposure of CTP has lead to increased cancer risk. Tensor total variation (TTV)[1] has been proposed to stabilize the quantification of perfusion parameters by integrating the anatomical structure correlation. Yet the locality limitation of the neighborhood region has led to noticeable absence or inflation of the delicate structures which are critical indicators for the clinical diagnosis. In this work, we propose a non-local tensor total variation (NL-TTV) deconvolution method to by incorporating the long-range dependency and the global connections in the spatio-temporal domain

4-D Spatio-Temporal MR Perfusion Deconvolution via Tensor Total Variation.

Conference
Ruogu Fang
ISMRM'15, International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, Toronto, Canada. Oral presentation
Publication Year: 2015

Introduction

4-D dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) is a well-established perfusion technique for non-invasive characterization of tissue dynamics, with promising applications in assessing a wide range of diseases, as well as monitoring response of therapeutic interventions). DSC-MRI provides critical real-time information by tracking the first-pass of an injected contrast-agent (e.g. gadolinium) with T2*-weighted MRI. The spatio-temproal data, consisting of contrast concentration signals for each voxel of a volume, are deconvolved from the arterial input function (AIF) and then post-processed to generate perfusion parameter maps, typically including the cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT) and time to peak (TTP). The most popular deconvolution method is truncated singular value decomposition (TSVD)1,2 and its variants3 , which fail to exploit the spatio-temporal nature of the 4D data with both the anatomical structure and the temporal continuation. This work adapts and demonstrates the feasibility of a 4-D tensor total variation (TTV) deconvolution approach, which has been proposed for CT perfusion4 , to brain MR perfusion, with evaluation on synthetic data and clinical DSC-MRI data for glioblastomas, the most common type of brain cancer. The method is guaranteed to convergence to global optimal because of the convex cost function and presents a more elegant framework of total variation for the deconvolution, compared to recent efforts5,6 which either do not have a global optimal solution for the non-convex case or need to handcraft spatial and temporal regularization terms.

Leveraging Coupled Multi-Index for Scalable Retrieval of Mammographic Masses.

Conference
Menglin Jiang, Shaoting Zhang, Ruogu Fang, Dimitris Metaxas.
ISBI'15, The IEEE International Symposium on Biomedical Imaging (ISBI), NYC, USA. Oral presentation 130 / 714 = 18%
Publication Year: 2015

A Spatio-Temporal Low-rank Total Variation Approach for Denoising Arterial Spin Labeling MRI Data

Conference
Ruogu Fang,Junzhou Huang, Wen-Ming Luh.
ISBI'15, The IEEE International Symposium on Biomedical Imaging (ISBI), NYC, USA
Publication Year: 2015

Abstract

Arterial spin labeling MRI (ASL-MRI) can provide quantitative signals correlated to the cerebral blood flow and neural activity. However, the low signal-to-noise ratio in ASL requires repeated acquisitions to improve the signal reliability, leading to prolonged scanning time. At fewer repetitions, noise and corruptions arise due to motion and physiological artifacts, introducing errors into the cerebral blood flow estimation. We propose to recover the ASL-MRI data from the noisy and corrupted observations at shorter scanning time with a spatio-temporal low-rank total variation method. The low-rank approximation uses the similarity of the repetitive scans, and the total variation regularization considers the local spatial consistency. We compare with the state-of-art robust M-estimator for ASL cerebral blood flow map estimation. Validation on simulated and real data demonstrate the robustness of the proposed method at fewer scanning repetitions and with random corruption

Improving Low-Dose Blood-Brain Barrier Permeability Quantification Using Sparse High-Dose Induced Prior for Patlak Model.

Journal
Ruogu Fang, Kolbeinn Karlsson, Tsuhan Chen, Pina C. Sanelli.
MedIA'14, Medical Image Analysis, Volume 18, Issue 6, Pages 866-880
Publication Year: 2014

Abstract

Blood-brain barrier permeability (BBBP) measurements extracted from the perfusion computed tomography (PCT) using the Patlak model can be a valuable indicator to predict hemorrhagic transformation in patients with acute stroke. Unfortunately, the standard Patlak model based PCT requires excessive radiation exposure, which raised attention on radiation safety. Minimizing radiation dose is of high value in clinical practice but can degrade the image quality due to the introduced severe noise. The purpose of this work is to construct high quality BBBP maps from low-dose PCT data by using the brain structural similarity between different individuals and the relations between the high- and low-dose maps. The proposed sparse high-dose induced (shd-Patlak) model performs by building a high-dose induced prior for the Patlak model with a set of location adaptive dictionaries, followed by an optimized estimation of BBBP map with the prior regularized Patlak model. Evaluation with the simulated low-dose clinical brain PCT datasets clearly demonstrate that the shd-Patlak model can achieve more significant gains than the standard Patlak model with improved visual quality, higher fidelity to the gold standard and more accurate details for clinical analysis.

Tensor Total-Variation Regularized Deconvolution for Efficient Low-Dose CT Perfusion.

Conference
Ruogu Fang,Pina Sanelli, Shaoting Zhang, Tsuhan Chen.
MICCAI'14, The 17th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Boston, USA
Publication Year: 2014

Abstract

Acute brain diseases such as acute stroke and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain' is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation will lead to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. We propose a novel efficient framework using tensor totalvariation (TTV) regularization to achieve both high efficiency and accuracy in deconvolution for low-dose CTP. The method reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with estimation error reduced by 40%. It also corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), at both normal and reduced sampling rate. An efficient

Anisotropic Tensor Total Variation Regularization For Low Dose Low CT Perfusion Deconvolution.

Conference
Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
MICCAI'14, The 17th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Workshop on Sparsity Techniques in Medical Imaging, Boston, USA
Publication Year: 2014

Abstract

Tensor total variation (TTV) regularized deconvolution has been proposed for robust low radiation dose CT perfusion. In this paper, we extended TTV algorithm with anisotropic regularization weighting for the temporal and spatial dimension. We evaluated TTV algorithm on synthetic dataset for bolus delay, uniform region variability and contrast preservation, and on clinical dataset for reduced sampling rate with visual and quantitative comparison. The extensive experiments demonstrated promising results of TTV compared to baseline and state-of-art algorithms in low-dose and low sampling rate CTP deconvolution with insensitivity to bolus delay. This work further demonstrates the effectiveness and potential of TTV algorithm's clinical usage for cerebrovascular diseases with significantly reduced radiation exposure and improved patient safety.

Towards Robust Deconvolution in Medical Imaging: Informatics, Diagnosis and Treatment.

PhD Thesis
Ruogu Fang
School of Electrical and Computer Engineering, Cornell University
Publication Year: 2014

Introduction

Robust deconvolution, the task of estimating hemodynamic parameters from measured spatio-temporal data, is a key problem in computed tomography perfusion. Traditionally, this has been accomplished by solving the inverse problem of the temporal tracer enhancement curves at each voxel inde- pendently. Incorporating spatial contextual information, i.e. information other than the temporal enhancement of the contrast agent, has received significant attention in recent works. Intra-subject contextual information is often exploited to remove the noise and artifacts in the low-dose hemodynamic maps. In this thesis, we take a closer look at the role of inter-subject contextual information in robust deconvolution. Specifically, we explore its importance in three as- pects. First: Informatics acquisition. We show, through synthetic evaluation as well as in-vivo clinical data, that inter-subject similarity provides complimen- tary information to improve the accuracy of cerebral blood flow map estimation and increase the differentiation between normal and deficit tissue. Second: Dis- ease diagnosis. We show that apart from the global learned dictionary for hemo- dynamic maps, the tissue-specific dictionaries can be effectively leveraged for disease diagnosis tasks as well, especially for low-contrast tissue types where the deficits usually occur. Lastly: Treatment plan. We propose a generalized framework with inter-subject context through dictionary learning and sparse representation possible for any hemodynamic parameter estimation, such as blood-brain-barrier permeability. We also extend to include inter-subject context through tensor total variation. The diverse hemodynamic maps provide necessary information for treatment plan decision making. We present results of our approaches on a variety of datasets and clinical tasks, such as uniform regions estimation, contrast preservation, data acquired at low-sampling rate and low radiation dose levels.

Towards Robust Deconvolution of Low-Dose Perfusion CT: Sparse Perfusion Deconvolution Using Online Dictionary Learning

Journal
Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
MedIA'13, Medical Image Analysis, Volume 17, Issue 4, Pages 417-428(5 Year Impact Factor: 4.512)
Publication Year: 2013

Abstract

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain.

Tissue-Specific Sparse Deconvolution for Low-Dose CT Perfusion

Conference
Ruogu Fang>, Tsuhan Chen, Pina C. Sanelli
MICCAI'13, The 16th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Japan
Publication Year: 2013

Abstract

Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra usually occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we extend this line of work by introducing tissuespecific sparse deconvolution to preserve the subtle perfusion information in the low-contrast tissue classes by learning tissue-specific dictionaries for each tissue class, and restore the low-dose perfusion maps by joining the tissue segments reconstructed from the corresponding dictionaries. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method with the advantage of better differentiation between abnormal and normal tissue in these patients.

Kinship Classification by Modeling Facial Feature Heredity

Conference
Ruogu Fang, Andrew C. Gallagher, Tsuhan Chen, Alexander Loui
IEEE International Conference on Image Processing, Melbourne, Australia
Publication Year: 2013

Sparsity-Based Deconvolution of Low-Dose Perfusion CT Using Learned Dictionaries.

Conference
Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
MICCAI'12, The 15th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Nice, France. Lecture Notes in Computer Science Volume 7510, 2012, pp 272-280.
Publication Year: 2012

Sparsity-Based Deconvolution Of Low-Dose Brain Perfusion CT In Subarachnoid Hemorrhage Patients

Conference
Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
ISBI'12, The 9th International Symposium on Biomedical Imaging, pp. 872-875, Barcelona, Spain. Oral presentation.
Publication Year: 2012

Abstract

Functional imaging serves as an important supplement to anatomical imaging modalities such as MR and CT in modern health care. In perfusion CT (CTP), hemodynamic parameters are derived from the tracking of the first-pass of the contrast bolus entering a tissue region of interest. In practice, however, the post-processed parametric maps tend to be noisy, especially in low-dose CTP, in part due to the noisy contrast enhancement profile and oscillatory nature of results generated by current computational methods. In this paper, we propose a sparsity-based perfusion parameter deconvolution approach that consists of a non-linear processing based on sparsity prior in terms of residue function dictionaries. Our simulated results from numericaldata and experiments in aneurysmal subarachnoid hemorrhage patients with clinical vasospasm show that the algorithm improves the quality and reduces the noise of the perfusion parametric maps in low-dose CTP, compared to state-of-the-art methods

Radiation dose reduction in computed tomography perfusion using spatial-temporal Bayesian methods.

Conference
Ruogu Fang, Ashish Raj, Tsuhan Chen, Pina C. Sanelli
SPIE'12, In Proceedings of SPIE Medical Imaging, Volume 8313, Paper #831345
Publication Year: 2012

Abstract

In current computed tomography (CT) examinations, the associated X-ray radiation dose is of significant concern to patients and operators, especially CT perfusion (CTP) imaging that has higher radiation dose due to its cine scanning technique. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) parameter as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and degrade CT perfusion maps greatly if no adequate noise control is applied during image reconstruction. To capture the essential dynamics of CT perfusion, a simple spatial-temporal Bayesian method that uses a piecewise parametric model of the residual function is used, and then the model parameters are estimated from a Bayesian formulation of prior smoothness constraints on perfusion parameters. From the fitted residual function, reliable CTP parameter maps are obtained from low dose CT data. The merit of this scheme exists in the combination of analytical piecewise residual function with Bayesian framework using a simpler prior spatial constrain for CT perfusion application. On a dataset of 22 patients, this dynamic spatial-temporal Bayesian model yielded an increase in signal-tonoise-ratio (SNR) of 78% and a decrease in mean-square-error (MSE) of 40% at low dose radiation of 43mA.

System and Method For Interactive Segmentation On Mobile Devices in a Cloud Computing Environment

Patents
Ruogu Fang,Leo Grady, Gianluca Paladini.
Siemens Corporation. Patent No: US20130272587 A1, WO2012027259 A2, WO2012027259 A3, approved on 4/19/2012
Publication Year: 2012

Abstract

A mobile device (160) for medical image analysis is disclosed. The mobile device (160) includes a display (162), a communication module (218), a memory (204) configured to store processor-executable instructions (224) and a processor (202) in communication with the display (162), the communication module (218) and the memory (204). The processor (202) being configured to execute the processor-executable instructions (224) to implement a compression routine to generate a compressed representation of a medical image stored in the memory (204), transmit the compressed representation to a remote device (110) via the communication module (218), receive segmented results from the remote device (110), wherein the segmented results are derived from a reconstruction of the compressed representation generated at the remote device (110), and present, via the display (162), a segmented medical image based on the received segmented results.

Segmentation of Liver Tumor Using Efficient Global Optimal Tree Metrics Graph Cuts

Conference
Ruogu Fang, Ramin Zabih, Ashish Raj, Tsuhan Chen
MICCAI'11, Abdominal Imaging, International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 51-59
Publication Year: 2011

Abstract

We propose a novel approach that applies global optimal tree-metrics graph cuts algorithm on multi-phase contrast enhanced contrast enhanced MRI for liver tumor segmentation. To address the difficulties caused by low contrasted boundaries and high variability in liver tumor segmentation, we first extract a set of features in multi-phase contrast enhanced MRI data and use color-space mapping to reveal spatial-temporal information invisible in MRI intensity images. Then we apply efficient tree-metrics graph cut algorithm on multi-phase contrast enhanced MRI data to obtain global optimal labeling in an unsupervised framework. Finally we use tree-pruning method to reduce the number of available labels for liver tumor segmentation. Experiments on realworld clinical data show encouraging results. This approach can be applied to various medical imaging modalities and organs.

Towards Computational Models of Kinship Verification.

Conference
Ruogu Fang, Kevin D. Tang, Noah Snavely, Tsuhan Chen.
ICIP'10, The 17th IEEE International Conference on Image Processing, Hong Kong. Oral presentation ICIP 2010 Best Paper Award
Publication Year: 2010

Abstract

We tackle the challenge of kinship verification using novel feature extraction and selection methods, automatically classifying pairs of face images as “related” or “unrelated” (in terms of kinship). First, we conducted a controlled online search to collect frontal face images of 150 pairs of public figures and celebrities, along with images of their parents or children. Next, we propose and evaluate a set of low-level image features that for use in this classification problem. After selecting the most discriminative inherited facial features, we demonstrate a classification accuracy of 70.67% on a test set of image pairs using K-Nearest-Neighbors. Finally, we present an evaluation of human performance on this problem.

Tree-Metrics Graph Cuts For Brain MRI Segmentation With Tree Cutting.

Conference
Ruogu Fang, Joyce Yu-hsin Chen, Ramin Zabih, Tsuhan Chen.
WNYIPW'10, IEEE Western New York Image Processing Workshop, pp. 10-13, Rochesester, NY. USA. Oral presentation
Publication Year: 2010

Abstract

We tackle the problem of brain MRI image segmentation using the tree-metric graph cuts (TM) algorithm, a novel image segmentation algorithm, and introduce a “tree-cutting” method to interpret the labeling returned by the TM algorithm as tissue classification for the input brain MRI image. The approach has three steps: 1) pre-processing, which generates a tree of labels as input to the TM algorithm; 2) a sweep of the TM algorithm, which returns a globally optimal labeling with respect to the tree of labels; 3) post-processing, which involves running the “tree-cutting” method to generate a mapping from labels to tissue classes (GM, WM, CSF), producing a meaningful brain MRI segmentation. The TM algorithm produces a globally optimal labeling on tree metrics in one sweep, unlike conventional methods such as EMS and EM-style geo-cuts, which iterate the expectation maximization algorithm to find hidden patterns and produce only locally optimal labelings. When used with the “tree-cutting” method, the TM algorithm produces brain MRI segmentations that are as good as the Unified Segmentation algorithm used by SPM8, using a much weaker prior. Comparison with the current approaches shows that our method is faster and that our overall segmentation accuracy is better.

Adaptive Scale Robust Feature Density Approximation For Visual Object Representation And Tracking

Conference
Chongyang Liu, Ruogu Fang, and Nelson H.C.Yung
IEEE International Conference on Computer Vision Theory and Applications, Lisboa, Portugal
Publication Year: 2009

Current Teaching

  • 2021 Fall

    BME3053C Computer Applications for BME

    The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

Teaching History

    • 2021 Spring

      BME6938 Multimodal Data Mining

      Course Objectives: 1. Understand multimodal data mining in the biomedical domain; 2. Understand the concept, approaches, and limitations in analyzing different modalities of biomedical data. 3. Learn to use biomedical data programming libraries and skills to analyze multimodal biomedical data.

      Instructor Evaluation: 4.92/5.00, Course Evaluation: 4.70/5.00

    • 2020 Fall

      BME3053C Computer Applications for BME

      The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

      Instructor Evaluation: 4.83/5.00, Course Evaluation: 4.53/5.00 (Historical highest for this course)

    • 2019 Spring

      BME6938 Multimodal Data Mining

      Course Objectives: 1. Understand multimodal data mining in the biomedical domain; 2. Understand the concept, approaches, and limitations in analyzing different modalities of biomedical data. 3. Learn to use biomedical data programming libraries and skills to analyze multimodal biomedical data.

      Instructor Evaluation: 4.87/5.00

    • 2019 Fall

      BME3053C Computer Applications for BME

      The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

    • 2018 Fall

      BME3053C Computer Applications for BME

    • 2017 Fall

      BME3053C Computer Applications for BME

      The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

    2017 Spring

    CAP 5771 Principles of Data Mining

  • 2017 Spring

    CAP 4770 Introduction to Data Mining

  • 2016 Fall

    CAP 4770 Introduction to Data Mining

  • 2016 Spring

    CAP 5610 Machine Learning

  • 2015 Fall

    CAP 4770 Introduction to Data Mining

    Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It has gradually matured as a discipline merging ideas from statistics, machine learning, database and etc. This is an introductory course for junior/senior computer science undergraduate students on the topic of Data Mining. Topics include data mining applications, data preparation, data reduction and various data mining techniques (such as association, clustering, classification, anomaly detection).

  • 2015 Spring

    CAP 5610 Machine Learning

    Machine learning is concerned with the question of how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For examples, machine learning techniques are used to create spam filters, to analyze customer purchase data, to understand natural language, or to detect fraudulent credit card transactions.

Filter by type:

EEGAILab

CodeToolbox for analyzing and visualizing dipole sources from resting electroencephalography data.

Citation

  • Kyle B. See, Rachel Ho, Stephen Coombes, Ruogu Fang, “TL1 Team Approach to Predicting Response to Spinal Cord Stimulation for Chronic Low Back Pain”, Journal of Clinical and Translational Science, Mar 2021.
  • Kyle B. See, Rachel Louise Mahealani Judy, Stephen Coombes, Ruogu Fang, “TL1 Team Approach to Predicting Short-term and Long-term Effects of Spinal Cord Stimulation” Journal of Clinical and Translational Science, Jul 2020.

Domain Shift

CodeAdversarial Discriminative Adaptation and Ensembling Collaborative Learning for Domain Shift in Unsupervised Optic Disc and Cup Segmentation

Citation

  • Peng Liu, Bin Kong, Zhongyu Li, Shaoting Zhang, and Ruogu Fang, 2019. CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation. Medical Image Analysis and Computer Assisted Intervention.

CT Perfusion Toolbox

CodeToolbox for CT perfusion image processing and computation, including image loading, preprocessing, reconstruction, quantification, and example code.

Citation

  • Fang, R., Chen, T. and Sanelli, P.C., 2013. Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning. Medical image analysis, 17(4), pp.417-428.
  • Fang, R., Zhang, S., Chen, T. and Sanelli, P.C., 2015. Robust low-dose CT perfusion deconvolution via tensor total-variation regularization. IEEE transactions on medical imaging, 34(7), pp.1533-1548.

Tensor Total Variation

CodeToolbox for Tensor Total Variation quantfication of CT perfusion parameters in low-dose CT perfusion.

Citation

  • Fang, R., Zhang, S., Chen, T. and Sanelli, P.C., 2015. Robust low-dose CT perfusion deconvolution via tensor total-variation regularization. IEEE transactions on medical imaging, 34(7), pp.1533-1548.
  • Fang, R., Sanelli, P.C., Zhang, S. and Chen, T., 2014, September. Tensor total-variation regularized deconvolution for efficient low-dose CT perfusion. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 154-161). Springer, Cham.

Sparse High-Dose Induced Patlak Model for Permeability

CodeSparse high-dose induced Patlak model for Permeability quantification in low-dose CT perfusion.

Citation

  • Fang, R., Karlsson, K., Chen, T. and Sanelli, P.C., 2014. Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model. Medical image analysis, 18(6), pp.866-880.

Sparse Perfusion Deconvolution Toolbox

CodeSparse perfusion deconvolution toolbox using dictionary learning and sparse coding for low-dose CT perfusion parameters quantification.

Citation

  • Fang, R., Chen, T. and Sanelli, P.C., 2012, October. Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 272-280). Springer Berlin Heidelberg.
  • Fang, R., Chen, T. and Sanelli, P.C., 2013. Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning. Medical image analysis, 17(4), pp.417-428.

Kinship Classification Dataset

DataFamily101 Dataset: first large-scale dataset of families across several generations. It contains 101 different family with distinct family names, including 206 nuclear families, 607 individuals, with 14,816 images.

Citation

  • Ruogu Fang, Andrew C. Gallagher, Tsuhan Chen, Alexander Loui. Kinship Classification by Modeling Facial Feature Heredity. IEEE International Conference on Image Processing, Australia, 2013 (ICIP '13)

Kinship Verification Dataset

DataCornell Kinship Verification Dataset (143 pairs of parents and children)

Citation

  • Ruogu Fang, Kevin D. Tang, Noah Snavely, Tsuhan Chen. Towards Computational Models of Kinship Verification. IEEE International Conference on Image Processing, Hong Kong, September 2010 (ICIP '10)

April 21, 2022

UF Innovate - Tech Tuesday

In today's Tech Tuesday, UF Innovate host Lauren Asmus introduces us to biomedical engineer, Dr. Ruogu Fang. Dr. Fang is an assistant professor at the University of Florida who made an important discovery when studying the brain and the eye.

March 8, 2022

Fang recieves HWCOE 2022 Faculty Award for Excellence in Innovation.

As a key faculty member of HWCOE in the space of data science and AI, Dr. Fang’s innovative research output and impact is fundamental to the latest HWCOE initiative to embrace AI-driven innovation.

February 2, 2022

UF, NVIDIA partner to speed brain research using AI

University of Florida researchers joined forces with scientists at NVIDIA, UF’s partner in its artificial intelligence initiative, and the OpenACC organization to significantly accelerate brain science as part of the Georgia Tech GPU Hackathon held last month.

January 10, 2022

Artificial Intelligence Prevents Dementia?

A team at the University of Florida is using targeted transcranial direct current stimulation to save memories. “It’s a weak form of electrical stimulation applied to the scalp. And this weak electric current actually has the ability to alter how the neurons behave,” continued Woods.

December 6, 2021

UF study shows artificial intelligence’s potential to predict dementia

New research published today shows that a form of artificial intelligence combined with MRI scans of the brain has the potential to predict whether people with a specific type of early memory loss will develop Alzheimer’s disease or other form of dementia.

July 19, 2021

UF researchers fight dementia using AI technology to develop new treatment

If presented with current models of electrical brain stimulation a decade ago, Dr. Adam J. Woods would have thought of a science-fiction movie plot. Artificial intelligence now pulls the fantasy out of screens and into reality.

July 16, 2021

Staving off dementia is focus of University of Florida study

Researchers at the University of Florida have found a therapy that holds promise for preventing dementia by combining non-invasive brain stimulation with brain games.

July 14, 2021

UF researchers using artificial intelligence to develop treatment to prevent dementia

UF researchers are developing a method they hope will prevent Alzheimer’s and Dementia. Dr. Ruogu Fang and Dr. Adam Woods are working to personalize brain stimulation treatments to make them as effective as possible.

July 13, 2021

UF researchers use AI to develop precision dosing for treatment aimed at preventing dementia

UF researchers studying the use of a noninvasive brain stimulation treatment paired with cognitive training have found the therapy holds promise as an effective, drug-free approach for warding off Alzheimer’s disease and other dementias.

February 2021

Our eyes may provide early warning signs of Alzheimer’s and Parkinson’s

Forget the soul — it turns out the eyes may be the best window to the brain. Changes to the retina may foreshadow Alzheimer’s and Parkinson’s diseases, and researchers say a picture of your eye could assess your future risk of neurodegenerative disease.

December 4, 2020

Eye Blood Vessels May Diagnose Parkinson's Disease

A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson's disease, according to research being presented at the annual meeting of the Radiological Society of North America.

December 1, 2020

UF researchers are looking into the eyes of patients to diagnose Parkinson's Disease

With artificial intelligence (AI), researchers have moved toward diagnosing Parkinson’s disease with, essentially, an eye exam. This relatively cheap and non-invasive method could eventually lead to earlier and more accessible diagnoses.

November 29, 2020

Blood Vessels in the Eye May Diagnose Parkinson's Disease

Using an advanced machine-learning algorithm and fundus eye images, which depict the small blood vessels and more at the back of the eye, investigators are able to classify patients with Parkinson's disease compared against a control group.

November 29, 2020

Eye exam possible test to determine Parkinson's disease

Scientists have determined a simple eye exam combined with powerful artificial intelligence (AI) machine learning technology that could provide early detection of Parkinson's disease.

November 25, 2020

Scientists Are Looking Into The Eyes Of Patients To Diagnose Parkinson’s Disease

With artificial intelligence (AI), researchers have moved toward diagnosing Parkinson's disease with, essentially, an eye exam. This relatively cheap and non-invasive method could eventually lead to earlier and more accessible diagnoses.

November 23, 2020

Eye Exam Could Lead to Early Parkinson’s Disease Diagnosis

A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson’s disease, according to research being presented at the annual meeting of the Radiological Society of North America.

November 23, 2020

Simple Eye Exam With Powerful Artificial Intelligence Could Lead to Early Parkinson’s Disease Diagnosis

A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson’s disease, according to research being presented at the annual meeting of RSNA.

November 23, 2020

RSNA 20: AI-Based Eye Exam Could Aid Early Parkinson’s Disease Diagnosis

A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson’s disease, according to research being presented at the annual meeting of the Radiological Society of North America.

May 31, 2017

Fang selected to ACM's Future of Computer Academy

Dr. Ruogu Fang, incoming assistant professor in the J. Crayton Pruitt Family Department of Biomedical Engineering, has been selected as a member of the Association for Computing Machinery’s (ACM) inaugural Future Computing Academy (FCA).

May 31, 2016

Researcher says head CT radiation dose can be reduced by 90 percent

Working with radiologists at Weill Cornell Medicine, Dr. Ruogu Fang, Ph.D., has applied machine learning and mathematical algorithms to manipulate low-dose CT perfusion images on stroke patients.

May 25, 2016

Professor uses computer science to reduce patients' exposure to radiation from CT scans

When a doctor orders a CT perfusion scan, most people don’t give it a second thought as it’s necessary to evaluate serious medical conditions such as stroke.

December 7, 2011

Facial recognition software spots family resemblance

FACIAL recognition software that’s as good as people at spotting family resemblances could help to reunite lost family members – or help the likes of Facebook work out which of your friends are blood relatives.

For Prospective Students

Postdoc

We are hiring a Postdoc Researcher from October 2021. Proficiency with Python and deep learning is required. Experience with Pytorch/Tensorflow, FSL/FreeSurfer/SPM are preferred. Please check out the job at https://facultyjobs.hr.ufl.edu/posting/97755.

Ph.D. Applicants:

For Ph.D. applicants interested in working in my lab, you can apply for Ph.D. program in Biomedical Engineering, Electrical and Computer Engineering, Computer Science, or other related programs at UF.

You are welcome to send me an email (ruogu dot fang at bme dot ufl dot edu) with the title "PhD Applicant: YOUR NAME - YOUR UNIVERSITY" and include your CV, transcript, and overview of research experiences before you apply.

Current or Incoming Master Students:

If you are already a master student or have received admission to BME/ECE/CISE or related programs at the Universit of Florida, you can contact me via email with your CV and transcript with an intention for voluntary research.

Undergraduate Students:

We take undergraduate from all years, but prefer students who have a good math and coding background, or at least the strong motivation and interest to build a solid math and coding background. Please send me an email including your CV, transcript, and a brief research statement of why you want to join SMILE lab as an undergraduate researcher, what related research projects you have been involved, and why you think you will be a good undergraduate researcher in our lab.

Prerequisite: Complete Coursera Machine Learning course by Andrew Ng before you can officially join the lab. (Free)

Co-requisite: Students who have joined our lab (including PhD, MS, and UG) are strongly encouraged to complete the following courses online during their time in the lab:


NSF Research Experience for Undergraduate (REU)

  • The Research Experiences for Undergraduates (REU) site at University of Florida (UF) provides research experience to undergraduate students a six-month/1-year program.
  • Students will work on cutting-edge research in the area of AI for brain health and brain-inspired AI under the supervision of Dr. Fang and her graduate students.
  • Improve oral and written comprehension and communication of technical knowledge.
  • Spark an interest in continuing STEM research in graduate schools.

Requirements

  • Applicant must be a U.S. citizen or permanent resident of the United States.
  • Applicant must be and remain an undergraduate student in good standing.

All students that meet these requirements are welcome to apply! We are particularly interested in broadening participation of underrepresented groups in STEM, and women and minorities are especially encouraged to apply. We also aim to provide research experiences for students who have limited exposure and opportunities to participate in research.

How to apply

  • Send an email to Dr. Fang entitled "REU Application_YourName" with your (i) a CV; (ii) information describing your research skills and experience; (iii) your goals and research interests for your REU research; (iv) contact details for two references. Applicants are considered on a rolling basis until filled. The deadline for 2022 application is April 1, 2022.
  • We consider applications on a rolling basis until all positions are filled.

We look forward to your smile at SMILE Lab to make your day!

At My Office

J287 Biomedical Science Building
University of Florida
1275 Center Dr.
Gainesville, FL, 32611.