Towards Generalizable and Robust Multimodal AI for Healthcare
Speaker:
Dr. CHEN Cheng
Postdoctoral Research Fellow
Harvard Medical School
Abstract:
Artificial Intelligence (AI) is catalyzing a paradigm shift in healthcare, promising to reshape the landscape of patient care. At the heart of this transformation is medical imaging, where AI-enabled technologies hold substantial promise for precise and personalized image-based diagnosis and treatment. Despite these advances, these models often underperform at real-world deployment, particularly due to the heterogeneous data distributions and varying modalities in healthcare applications. In this talk, I will introduce our work dedicated to tackling these real-world challenges to advance model generalizability and multimodal robustness. First, I will show how we can leverage generative networks and model adaptation to generalize models under data distribution shifts. Next, I will describe how to achieve robust multimodal learning with missing modalities and with imaging and non-imaging clinical information. Finally, I will present our work that extends to large-scale datasets and more diverse modalities based on foundation model for generalizable multimodal representation learning.
Biography:
Dr. Cheng CHEN is 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 in 2021. She received her M.S. and B.S. degrees from Johns Hopkins University and Zhejiang University, respectively. Her research interests lie in the interdisciplinary area of AI and healthcare, with a focus on generalizable, robust, and multimodal medical image analysis. She has over 25 papers published at top AI and medical imaging venues, reaching over 2300 Google Scholar citations with an h-index of 16. Her first-authored papers have been recognized as an ESI “Highly cited paper”, selected as oral presentations, and received travel awards from AAAI and MICCAI. She has been named one of 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 2024 and reviewer for multiple top journals and conferences.
Enquiries:
Mr. WONG O-Bong (obong@cse.cuhk.edu.hk)
Ms. FUNG Wing Chi Mary (maryfung@cse.cuhk.edu.hk)