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🎸A full-day feast of medical computer vision, at Music City!🎶

Program

8:20-8:30 am Yuankai Huo

Keynote session I (chair: Daniel Moyer)

8:30-9:00 am Katherine Van Schaik

Katherine D. Van Schaik, MD PhD MA, is an Assistant Professor of Radiology, Electrical and Computer Engineering, and Classical and Mediterranean Studies at Vanderbilt. She received her BA, MA, MD, and PhD from Harvard and completed her radiology residence at Beth Israel Deaconess Medical Center/Harvard Medical School. She completed her fellowship in Musculoskeletal radiology at Vanderbilt before joining us here as faculty. Her research, supported by the US National Science Foundation, the Wellcome Trust, and the Society of Skeletal Radiology, among other organizations, explores skeletal aging, particularly osteoporosis, using imaging technologies and epigenetics. She leads Vanderbilt’s Program in Health over Time, which investigates human frailty and resilience through interdisciplinary approaches spanning lifetimes and human history, with a focus on the skeletal aging.

9:00-9:30 am Haibin Ling

Haibin Ling received the B.S. and M.S. degrees from Peking University in 1997 and 2000, respectively, and the Ph.D. degree from the University of Maryland, College Park, in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia. From 2006 to 2007, he worked as a postdoctoral scientist at the University of California Los Angeles. In 2007, he joined Siemens Corporate Research as a research scientist; then, from 2008 to 2019, he worked as an Assistant Professor and then Associate Professor at Temple University. In fall 2019, he joined Stony Brook University as a SUNY Empire Innovation Professor in the Department of Computer Science. His research interests include computer vision, augmented reality, medical image analysis, machine learning, and AI for science. He received Best Student Paper Award at ACM UIST (2003), Best Journal Paper Award at IEEE VR (2021), NSF CAREER Award (2014), Yahoo Faculty Research and Engagement Award (2019), and Amazon Machine Learning Research Award (2019). He serves or served as Associate Editors for IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), IEEE Trans. on Visualization and Computer Graphics (TVCG), Computer Vision and Image Understanding (CVIU), and Pattern Recognition (PR), and as Area Chairs various times for CVPR, ICCV, ECCV, ACM MM and WACV. He is a fellow of IEEE.

9:30-10:00 am Siqi Liu

Siqi Liu is the VP of AI at Paige in New York, where he leads AI product development and research. Before joining Paige, he worked on radiology AI at Siemens Healthineers in Princeton. He holds a PhD in Computer Science from the University of Sydney.

10:00-10:30 Coffee Break

Keynote session II (chair: Jie Ying Wu)

10:30-11:00 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.

11:00-11:30 am Faisal Mahmood

Dr. Faisal Mahmood is an Associate Professor at Harvard Medical School and the Division of Computational Pathology at Brigham and Women's Hospital and Massachusetts General Hospital. He is a full member of the Dana-Farber Cancer Institute / Harvard Cancer Center ; an Associate Member of the Broad Institute of Harvard and MIT, and a member of the Harvard Bioinformatics and Integrative Genomics (BIG) faculty. His laboratory works on developing generative and agentic AI algorithms, methods and techniques for healthcare with a particular focus on disease diagnosis, prognosis and therapeutic response prediction. Dr. Mahmood's lab has developed several widely used methods and algorithms for digital and computational pathology, and his labs works has been published in major scientific journals. Mahmood is also a principal investigator on several large NIH and ARPA-H grants. Dr. Mahmood is also the Scientific Co-Founder of Modella AI a company with a focus on developing generative and agentic AI tools for healthcare.

11:30-12:00 pm Chao Chen

Chao Chen, Ph.D., is an Associate Professor in the Department of Biomedical Informatics at Stony Brook University, with affiliated appointments in the Departments of Computer Science and Applied Mathematics and Statistics. His research integrates biomedical imaging informatics, robust machine learning, and topological data analysis. He focuses on developing transparent and trustworthy learning methods by combining mathematical modeling with modern deep learning to analyze complex imaging data from pathology and radiology. Dr. Chen has published widely in top-tier venues and has received several honors, including the NSF CAREER Award and the Stony Brook Trustees Faculty Award.

12:00-1:30 Lunch Break

Keynote session III (chair: Roza Bayrak)

1:30-2:00 pm Zongwei Zhou

Zongwei Zhou is an assistant research scientist at Johns Hopkins University. He received his Ph.D. in Biomedical Informatics at Arizona State University in 2021, where he was honored with the President’s Award for Innovation. His research focuses on developing novel methods to reduce the annotation efforts for computer-aided detection and diagnosis. Zongwei received the AMIA Doctoral Dissertation Award in 2022, the Elsevier-MedIA Best Paper Award in 2020, and the MICCAI Young Scientist Award in 2019. He was named the top 2% of Scientists released by Stanford University in 2022-2024.

2:00-2:30 pm Lin Gu

Dr. Lin Gu is a research scientist at RIKEN AIP, Japan and a special researcher at the University of Tokyo. He is with specific interest and expertise in the application of Artificial Intelligence in Medical Imaging and Computational Photography. Before moving to Japan, he was a postdoctoral research fellow at A*STAR, Singapore working on machine learning on biomedical imaging. He is now the project manager for Moonshot Program on Continuous Learning and Memory Mechanism and ACT-X Program on Gaze Assisted AI. His research interest lies in simulating human’s neocortex to enhance AI, especially its application in medical fields.

2:30-3:00 Coffee Break

Clinical session: Clinical Needs for Computer Vision (chair: Michael Miga)

3:00-3:05 pm Michael Miga
3:05-3:25 pm Alexander Langerman

Alexander Langerman is a surgeon, academic, innovator and entrepreneur who studies the operating room and surgical profession through the lenses of ethics, innovation, data science, and efficiency. His research falls under the umbrella of “surgical transparency” – how to enhance the transfer of information into and out of the operating room for the benefit of patients, hospital staff, and the healthcare system with the goal of improving outcomes and building operational efficiency. More information at AlexanderLangerman.com

3:25-3:45 pm Nicholas Kavoussi

Nick Kavoussi MD is an assistant professor of Urology at Vanderbilt and was a Physician in Residence at the Vanderbilt Institute of Surgery and Engineering. His academic focus is on developing AI based smart tools for endoscopic surgery with support from the NIH as well as ARPA-H.

3:45-4:05 pm Michael Topf

Michael Topf is an Assistant Professor in the Department of Otolaryngology - Head and Neck Surgery at Vanderbilt University Medical Center and Biomedical Engineering at Vanderbilt University. His clinical interests include the surgical management and reconstruction of mucosal head and neck cancer including transoral robotic surgery (TORS). The goals of his research are to improve oncologic and functional outcomes in head and neck cancer patients through enhanced communication between surgeons and pathologists using 3D scanning technology and augmented reality surgery using 3D specimen holograms.

4:05-4:25 pm Eric Tkaczyk

Dr. Tkaczyk is a physician-scientist whose mission is to improve patient care with innovative technologies to rigorously assess disease via the skin. Dr. Tkaczyk is a graduate of the MD/PhD at the University of Michigan, with PhD in electrical engineering. Dr. Tkaczyk is active in the leadership of several conferences and international working groups related to artificial intelligence and dermatologic imaging technologies. His work has been recognized by the White House with a Presidential Early Career Award for Scientists and Engineers.