Welcome to the Multimodal Health Intelligence (MHl) Lab!

Our group is dedicated to the interdisciplinary field of artificial intelligence and healthcare, focusing on integrating multimodal and heterogeneous medical data for precision and personalized disease diagnosis, prognosis, and treatment decision-making. Our research focus includes: 1) medical VLMs/MLLMs; 2) biomedical data generation and medical world models; 3) agentic AI systems for intelligent healthcare; 4) time-series data analysis.

Highlights

SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignment

Authors: Weiren Zhao, Yi Dong, Cheng Chen

Venue: Forty-third International Conference on Machine Learning (ICML), 2026

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Compositional Perception and Generalizing Induction paper image

CoGe-GCD: Reframing Generalized Category Discovery with Compositional Generalization

Authors: Luyao Tang, Jiewei Zheng, Kunze Huang, Chaoqi Chen, Yue Huang, Cheng Chen

Venue: Forty-third International Conference on Machine Learning (ICML), 2026

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Bures-Isotropy Alignment: Manifold Learning of Generalized Category Discovery

Authors: Luyao Tang, Kunze Huang, Chaoqi Chen, Cheng Chen

Venue: The Fourteenth International Conference on Learning Representations (ICLR), 2026

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REFLEX-Med: Reinforcement with Label-Free Explainability for Unified Medical Reasoning

Authors: Luyao Tang, Zheyuan Cai, Qinong Tian, Zi Li, Quande Liu, Kyongtae Tyler Bae, Cheng Chen

Venue: 15th IEEE Image, Video, and Multidimensional Signal Processing Workshop (IEEE IVMSP), 2026 [Best Paper Award]

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MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

Authors: Cheng Chen, Juzheng Miao, Dufan Wu, Zhiling Yan, Sekeun Kim, Jiang Hu, Aoxiao Zhong, Zhengliang Liu, Lichao Sun, Xiang Li, Tianming Liu, Pheng-Ann Heng, Quanzheng Li

Venue: Medical Image Analysis (MedIA), 2024

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See our publications