Prof. Ran He (IAPR/IEEE Fellow)
Institute of Automation, Chinese Academy of Sciences, China

Ran He received his Ph.D. degree (2009) from National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA). He is currently a full Professor at NLPR, CASIA. His research interests involve the general areas of pattern recognition and computer vision, information forensics and security. He published over 200 items of academic work including conference papers, journal papers, and patents. His research won IEEE SPS young author best paper award, ICPR 2020 best scientific paper award, ACL 2025 outstanding paper award, ACPR 2025 best student paper award. His Google Scholar is 25,000+. Dr. Ran He is a Fellow of IAPR and Fellow of IEEE. He is currently vice president of IEEE Biometrics Council, vice chair of IEEE IFS-TC, Deputy Editors-in-Chief for IEEE Transactions on Information Forensics and Security (T-IFS), Associate Editors-in-Chief for IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM). He is also an Associate Editor for IEEE TPAMI, TIP, TCSVT, IJCV and Pattern Recognition.

 

 

Prof. Yen-Wei Chen
Ritsumeikan University, Japan

Yen-Wei Chen received the B.E. degree in 1985 from Kobe Univ., Kobe, Japan, the M.E. degree in 1987, and the D.E. degree in 1990, both from Osaka Univ., Osaka, Japan. He was a research fellow with the Institute for Laser Technology, Osaka, from 1991 to 1994. From Oct. 1994 to Mar. 2004, he was an associate Professor and a professor with the Department of Electrical and Electronic Engineering, Univ. of the Ryukyus, Okinawa, Japan. He is currently a professor with the college of Information Science and Engineering, Ritsumeikan University, Japan. He is the founder and the first director of Center of Advanced ICT for Medicine and Healthcare, Ritsumeikan University, Japan. Since April 2024, he has been a Foreign Fellow of the Engineering Academy of Japan. His research interests include medical image analysis, computer vision and computational intelligence. He has published more than 300 research papers in a number of leading journals and leading conferences including IEEE Trans. Image Processing, IEEE Trans. Medical Imaging, CVPR, ICCV, MICCAI. He has received many distinguished awards including ICPR2012 Best Scientific Paper Award, 2014 JAMIT Best Paper Award. He is/was a leader of numerous national and industrial research projects.

Speech Title: Trustworthy Deep Learning for Biomedical Image Analysis

Abstract: Deep learning has achieved remarkable performance in biomedical image analysis, yet its adoption in clinical practice remains limited by concerns over interpretability, reliability, and accountability. A central challenge is that purely data-driven models often lack the anatomical and clinical grounding that clinicians rely on when making diagnostic decisions. This talk argues that embedding domain knowledge—both anatomical priors and physicians' clinical experience—into deep learning pipelines is a principled path toward more trustworthy biomedical AI. First, I discuss how anatomical priors—organ position, shape, and topology—can be injected as deep atlas prior to improve interpretability and accuracy of organ segmentation. Second, I examine how physicians' experience and clinical knowledge can be integrated to image-based AI model to enhance reliability and robustness.