2 citations · 4 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
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…
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…
SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence
Zhitao Zeng, Zhu Zhuo, Xiaojun Jia +12
Foundation models have achieved transformative success across biomedical domains by enabling holistic understanding of multimodal data. However, their application in surgery remain…