报告题目：Knowledge-Guided Multimodal Deep Learning for Enhanced Medical Image Analysis

报告时间：2026年09月11日14:00

地点：勤园12-304

报告摘要：

Deep Learning (DL) has achieved remarkable success across various domains, particularly in computer vision and image recognition. In medical imaging, although DL models have demonstrated state-of-the-art performance, their translation into real-world clinical practice remains limited. This gap stems from two fundamental challenges: the scarcity of large-scale annotated datasets and the "black-box" nature of conventional DL models, which undermines clinical trust and interpretability. In this talk, I will present a paradigm shift from data-centric deep learning toward knowledge-guided multimodal learning—an approach that integrates clinical wisdom as an active component of the model itself, ad introduce two complementary strategies for infusing domain knowledge into deep learning:

(1)    Explicit knowledge guidance, which integrates unstructured radiology reports with medical images using cross-modal attention mechanisms to enhance both accuracy and explainability.

(2)  Implicit knowledge distillation, where genetic information serves as a teacher during training, transferring its predictive power to a student network that relies solely on CT images at inference time—enabling scalable, cost-effective precision diagnostics.

主讲人：

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.