CVPR2026 Medical Computer Vision Workshop

June 3 , 2026

Denver, Colorado

Full Keynote Series

Overview

Medical computer vision is reshaping the future of healthcare by uniting computer vision, machine learning, and clinical imaging into a shared scientific endeavor. Progress in this field is driven by cross-disciplinary collaboration, spanning algorithm design, imaging physics, robotics, and clinical translation, and is advancing our ability to understand, diagnose, and treat disease through intelligent visual reasoning. This workshop brings together leading experts from academia, healthcare, and industry whose research collectively spans foundation models, surgical vision, embodied AI, and data-centric learning. Through their perspectives, the event will promote open dialogue on key challenges in scalability, reliability, interpretability, and clinical integration. The broader impact of this workshop lies in shaping a unified research vision for trustworthy, human-centered, and globally accessible medical AI systems. By bridging technical innovation with clinical practice, it will catalyze new collaborations, establish shared benchmarks, and accelerate the translation of computer vision from research to real-world healthcare impact.

SCHEDULE (TBD)

Program

8:20-8:30 am Zongwei Zhou

Keynote session I (Moderator: Chenyu You)

8:30-8:50 am René Vidal

René Vidal, a global pioneer of data science, is the Rachleff University Professor, with joint appointments in the Department of Radiology in the Perelman School of Medicine and the Department of Electrical and Systems Engineering in the School of Engineering and Applied Science. Dr. Vidal has been named a Penn Integrates Knowledge University Professor at the University of Pennsylvania. René Vidal received his B.S. degree in Electrical Engineering (highest honors) from the Pontificia Universidad Catolica de Chile in 1997 and his M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2000 and 2003, respectively. He was a research fellow at the National ICT Australia in 2003 and joined The Johns Hopkins University in 2004 as a faculty member in the Department of Biomedical Engineering and the Center for Imaging Science.

8:50-9:10 am Mert R. Sabuncu

Mert R. Sabuncu received a PhD degree in Electrical Engineering from Princeton University, where his dissertation focused on entropy-based approaches to image registration. Mert then moved to the Massachusetts Institute of Technology for a post-doc with Polina Golland at the Computer Science and Artificial Intelligence Lab, where he worked on a range of biomedical image analysis problems, including the segmentation of brain MRI scans. After his post-doc at MIT, Mert spent a few years at the A.A Martinos Center for Biomedical Imaging (Massachusetts General Hospital and Harvard Medical School) as a junior faculty member, where he built a research program on algorithmic tools for integrating genetics and medical imaging. Today, Mert is a Professor in Electrical and Computer Engineering at Cornell University and Cornell Tech, in New York City. He also holds a dual appointment in Radiology at Weill Cornell Medicine, where he serves as the Vice Chair of AI and Engineering Research. His group develops machine learning based computational tools for biomedical imaging applications. He is a recipient of an NSF CAREER Award (2018) and an NIH Early Career Development Award (2011).

9:10-9:30 am Dimitris Metaxas

Dr. Dimitris Metaxas is a Distinguished Professor in the Department of Computer Science at Rutgers University since July 2007. From September 2001 to June 2007 he was a Professor in the same department. He is currently directing the Center for Computational Biomedicine, Imaging and Modeling (CBIM). From January 1998 to September 2001 he was a tenured Associate Professor in the Computer and Information Science Department of the University of Pennsylvania and Director of the VAST Lab. Prior to this he was an Assistant Professor in the same department since 1992. Prof. Metaxas received a Diploma in Electrical Engineering from the National Technical University of Athens of Athens Greece in 1986, an M.Sc. in Computer Science from the University of Maryland, College Park in 1988, and a Ph.D. in Computer Science from the University of Toronto, Ontario, Canada in 1992. Dr. Metaxas has been conducting research towards the development of formal methods upon which both computer vision, computer graphics and medical imaging can advance synergistically. In computer vision, he works on the simultaneous segmentation and fitting of complex objects, shape representation, statistical model-based tracking, learning, sparsity, ASL and gesture recognition. In particular he is focusing on human body and shape motion analysis, human surveillance, security applications, ASL recognition, behavior modeling and analysis and scalable solutions to large and distributed sensor-based networks.

9:30-9:50 am Sharon X. Huang

Sharon Xiaolei Huang received her B.E. degree in Computer Science from Tsinghua University, China, and her M.S. and Ph.D. degrees in Computer Science from Rutgers University. She is currently the David Reese Professor in the College of Information Sciences and Technology, and an affiliate member of the Huck Institutes of the Life Sciences and the Institute for Computational and Data Sciences at the Pennsylvania State University, University Park, PA. Her research interests are in the areas of artificial intelligence, computer vision, biomedical image computing, and machine learning, focusing on methods for image and video segmentation, image and video synthesis, 3D computer vision, object recognition, computer-assisted diagnosis and intervention, registration/matching, and motion tracking. Her broader interests include AI and data science for healthcare and biomedicine, biomedical informatics, computer graphics, visualization, and human-computer interaction. She regularly serves as an area chair and on the program committees of major conferences in medical image computing and computer vision and is an associate editor for several journals including Medical Image Analysis, and Computerized Medical Imaging and Graphics. Her research has been funded by the NIH, NSF, DOE, the Howard Hughes Medical Institute, and the Pennsylvania state.

Keynote session II (Moderator: Zongwei Zhou)

10:00-10:20 am Mathias Unberath

Mathias Unberath is an Assistant Professor in the Department of Computer Science at Johns Hopkins University with affiliations to the Laboratory for Computational Sensing and Robotics and the Malone Center for Engineering in Healthcare. He has created and is leading the Advanced Robotics and Computationally AugmenteD Environments (ARCADE) Lab that conducts research at the intersection of computer vision, machine learning, augmented reality, robotics, and medical imaging to develop collaborative systems that assist clinical professionals across the healthcare spectrum. Previously, Mathias was an Assistant Research Professor in Computer Science and postdoctoral fellow in the Laboratory for Computational Sensing and Robotics at Hopkins and completed his Ph.D. in Computer Science at the Friedrich-Alexander-Universität Erlangen-Nürnberg from which he graduated summa cum laude in 2017. While completing a Bachelor’s in Physics and Master’s in Optical Technologies at FAU Erlangen, Mathias studied at the University of Eastern Finland as an ERASMUS scholar in 2011 and joined Stanford University as a DAAD fellow throughout 2014. Mathias has published more than 80 journal and conference articles and has received numerous awards, grants, and fellowships, including the NIH NIBIB R21 Trailblazer Award.

10:20-10:50 am Vishal Patel

Vishal M. Patel is an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at Johns Hopkins University. His research focuses on computer vision, machine learning, image processing, medical image analysis, and biometrics. He has received a number of awards including the 2021 IEEE Signal Processing Society (SPS) Pierre-Simon Laplace Early Career Technical Achievement Award, the 2021 NSF CAREER Award, the 2021 IAPR Young Biometrics Investigator Award (YBIA), the 2016 ONR Young Investigator Award, and the 2016 Jimmy Lin Award for Invention. Patel serves as an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence journal and IEEE Transactions on Biometrics, Behavior, and Identity Science. He also chairs the conference subcommittee of IAPR Technical Committee on Biometrics (TC4). He is a fellow of the IAPR.

10:50-11:10 pm Archana Venkataraman

Prof. Archana Venkataraman is an Associate Professor of Electrical and Computer Engineering at Boston University. From 2016-2022, she was an Assistant Professor at Johns Hopkins University. Dr. Venkataraman directs the Neural Systems Analysis Laboratory and is affiliated with the Department of Biostatistics, the Department of Biomedical Engineering, the Center for Brain Recovery, and the Rafik B. Hariri Institute for Computing at Boston University. Dr. Venkataraman’s research lies at the intersection of biomedical imaging, artificial intelligence, and clinical neuroscience. Her work has yielded novel insights into debilitating neurological disorders, such as autism, schizophrenia, and epilepsy, with the long-term goal of improving patient care. Dr. Venkataraman completed her B.S., M.Eng., and Ph.D. in Electrical Engineering at MIT in 2006, 2007, and 2012, respectively. She is a recipient of the MIT Provost Presidential Fellowship, the Siebel Scholarship, the National Defense Science and Engineering Graduate Fellowship, the NIH Advanced Multimodal Neuroimaging Training Grant, numerous best paper awards, and the National Science Foundation CAREER award. Dr. Venkataraman was also named by MIT Technology Review as one of 35 Innovators Under 35 in 2019.

11:10-11:30 pm Jeremias Sulam

Jeremias Sulam is an assistant professor in the Department of Biomedical Engineering, and is also affiliated with the Mathematical Institute for Data Science and the Center for Imaging Science. He holds a secondary appointment in the Department of Applied Mathematics and Statistics and is a member of the Data Science and AI Institute. Jeremias’ research focuses on the foundations of machine learning and applications to biomedical imaging. He is interested in learning under parsimonious structures in data, robustness, and ethical implications of data-driven methods, as well as in the interpretability and auditing of the resulting models. His work is motivated by applications of diagnostic imaging in radiology, inverse problems, and biomarker discovery in neuroscience and digital pathology.

11:30-11:50 pm Kayhan Batmanghelich

Kayhan is an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University. My research focuses on medical image analysis and the broader application of artificial intelligence in healthcare. Previously, I was a faculty member in the Department of Biomedical Informatics at the University of Pittsburgh. I received my PhD from the University of Pennsylvania. I am a recipient of the NSF CAREER Award and a Google Academic Research Award.

Keynote session III (Moderator: TBD)

1:00 -1:20 pm Pallavi Tiwari

Pallavi Tiwari, PhD is an Associate Professor (tenure) in the Departments of Radiology, Biomedical Engineering, and Medical Physics; and serves as the Co-Director of Imaging and Radiation Science at the Carbone Cancer Center. Prior to joining UW–Madison, Dr. Tiwari was at Case Western Reserve University, where she was the director of the Brain Image Computing Laboratory and an Assistant Professor in the Department of Biomedical Engineering. Dr. Tiwari’s research interests lie in pattern recognition, data mining, and image analysis for automated computerized diagnostic, prognostic, and treatment evaluation solutions using radiologic imaging in oncology and neurological disorders. So far, her research has evolved into over 60 peer-reviewed publications, 50 peer-reviewed abstracts, and 15 patents (9 issued, 6 pending). Dr. Tiwari has been a recipient of several scientific awards, most notably, being named as one of 100 women achievers by the Government of India for making a positive impact in the field of Science and Innovation. In 2018, she was selected as one of Crain’s Cleveland Business Forty under 40. She has also been awarded the J&J Women in STEM (WiSTEM2D) scholar award in Technology, and the Honorary Early Career Achievement Award through the Society for Imaging Informatics in Medicine (SIIM). Most recently, Dr. Tiwari was inducted as a senior member of the National Academy of Inventors and named Vilas Distinguished Achievement Professor.

1:20-1:40 pm Ehsan Adeli

1:40-2:00 pm Curtis P. Langlotz

Prof. Curtis P. Langlotz is a Professor of Radiology, Medicine, and Biomedical Data Science at Stanford University, where he also serves as Senior Associate Vice Provost for Research and Director of the Center for Artificial Intelligence in Medicine and Imaging (AIMI Center). He is a Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and currently serves as President of the Radiological Society of North America (RSNA). Prof. Langlotz’s research focuses on developing deep learning and machine learning systems for medical imaging, aiming to reduce diagnostic errors and improve clinical decision-making. He has authored more than 200 scientific publications and the influential book The Radiology Report. His leadership has advanced the field through key initiatives such as the RadLex terminology standard, the RSNA report template library, and radiology communication standards. He has received numerous honors, including the GERRAF Career Development Award and the Lifetime Achievement Award from the Radiology Alliance for Health Services Research.

Industrial session: MCV at Production (Moderator: Yucheng Tang)

2:10-2:30 pm Hoifung Poon (Microsoft Healthcare AI)

Hoifung Poon is General Manager at Health Futures in Microsoft Research and an affiliated faculty at the University of Washington Medical School. He leads biomedical AI research and incubation, with the overarching goal of structuring medical data to optimize delivery and accelerate discovery for precision health.

2:30-2:50 pm Daguang Xu (Nvidia Medical Applied Research)

Daguang Xu is now a senior research manager at NVIDIA. He is leading a research team in healthcare AI, focusing on developing world-class machine learning and deep learning-based methods to solve the challenging problems in medical domain. His current research interest includes but not limited to medical imaging analysis, EHR analysis, computer aided diagnosis, federated learning. etc. He has published 100+ papers on top journals and conferences and has 50+ granted or in-application patents. His team has been working closely with many universities, hospitals, and medical centers, e.g. NIH, MGH, KCL, Children's national medical center, etc.

2:50-3:10 pm UII Speaker
3:10-3:30 pm Google Speaker