Full Professor, Shanghai Jiao Tong University
4 papers at NeurIPS 2025
We propose Retrieval-Augmented Diagnosis (RAD), a holistic method that explicitly integrates disease-centered medical knowledge into multimodal diagnosis models.
We propose a universal video grounding model based on MLLMs, which achieves superior accuracy, generalizability, and robustness.
