MHRC Research Seminar: Medical Imaging AI: From Specialist to Multimodal and Generalist Medical Intelligence

Enjoy free admission, all are welcome.
* Seats are limited and will be arranged on a first-come-first-served basis.
* Please note that NO CPD points will be offered by the research seminar.

Topic:
Medical Imaging AI: From Specialist to Multimodal and Generalist Medical Intelligence

Speaker:
Prof. Chen Cheng
Assistant Professor
Department of Electrical and Electronic Engineering
The University of Hong Kong

Abstract:
Artificial Intelligence (AI) is catalysing a paradigm shift in healthcare, with the potential to fundamentally transform the landscape of patient care. In particular, AI-driven medical imaging technologies have demonstrated remarkable promise in enabling precise, efficient, and personalised diagnosis and treatment. As medical data becomes increasingly diverse, integrating multimodal imaging and non-imaging information has emerged as a key frontier in advancing medical AI. In this talk, Prof. Chen will present her research work from task-specific specialist models to multimodal and general-purpose medical intelligence. Prof. Chen will highlight novel methodologies that address technical challenges associated with diverse medical data with representative applications in disease diagnosis, medical reasoning, and treatment decision-making. This talk will also discuss the future opportunities and challenges for multimodal AI and large foundation models in shaping the next generation of intelligent healthcare.

Biography:
Prof. Chen Cheng is an Assistant Professor at the Department of Electrical and Electronic Engineering, University of Hong Kong. Before joining HKU, she was a postdoc research fellow at the Center for Advanced Medical Computing and Analysis, Harvard Medical School. She obtained her Ph.D. degree in Computer Science and Engineering at The Chinese University of Hong Kong. She received her M.S. and B.S. degrees in Biomedical Engineering from Johns Hopkins University and Zhejiang University, respectively. Her research interests lie in the interdisciplinary area of AI and healthcare, with a focus on generalisable, robust, and multimodal medical image analysis. Her first-authored papers have been recognised as ESI “Highly cited paper”, selected as oral presentations, and received travel awards from AAAI and MICCAI. She has been recognised as World’s Top 2% Scientists by Stanford University, named the Global Top 80 Chinese Young Female Scholars in AI and won the MICCAI Federated Brain Tumor Segmentation Challenge. She also serves as Area Chair of MICCAI, Guest Editor of IEEE JBHI, and reviewer for multiple top-tier journals and conferences.