collaborators

7 papers

cs.CV2026

ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding

Hangjie Yuan, Yichen Qian, Zhiwei Tang +21

Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric chall…

cs.AI2026

Surg: A Spectrum of Large-Scale Multimodal Data and Foundation Models for Surgical Intelligence

Zhitao Zeng, Mengya Xu, Jian Jiang +13

Surgical intelligence has the potential to improve the safety and consistency of surgical care, yet most existing surgical AI frameworks remain task-specific and struggle to genera…

cs.CV2026

Surg-R1: A Hierarchical Reasoning Foundation Model for Scalable and Interpretable Surgical Decision Support with Multi-Center Clinical Validation

Jian Jiang, Chenxi Lin, Yiming Gu +24

Surgical scene understanding demands not only accurate predictions but also interpretable reasoning that surgeons can verify against clinical expertise. However, existing surgical…

cs.AI2026

SurgRAW: Multi-Agent Workflow with Chain of Thought Reasoning for Robotic Surgical Video Analysis

Chang Han Low, Ziyue Wang, Tianyi Zhang +4

Robotic-assisted surgery (RAS) is central to modern surgery, driving the need for intelligent systems with accurate scene understanding. Most existing surgical AI methods rely on i…

cs.CV2026

Multi-scale Temporal Prediction via Incremental Generation and Multi-agent Collaboration

Zhitao Zeng, Guojian Yuan, Junyuan Mao +3

Accurate temporal prediction is the bridge between comprehensive scene understanding and embodied artificial intelligence. However, predicting multiple fine-grained states of a sce…

cs.CV2025

Unleashing the Power of Image-Tabular Self-Supervised Learning via Breaking Cross-Tabular Barriers

Yibing Fu, Yunpeng Zhao, Zhitao Zeng +2

Multi-modal learning integrating medical images and tabular data has significantly advanced clinical decision-making in recent years. Self-Supervised Learning (SSL) has emerged as…