1 citations · 5 across the 26 of their papers we have counts for
10 papers · 1 filter
EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis
Yusheng Liao, Chaoyi Wu, Junwei Liu +12
Electronic Health Records (EHRs) contain rich yet complex information, and their automated analysis is critical for clinical decision-making. Despite recent advances of large langu…
Evolving Interactive Diagnostic Agents in a Virtual Clinical Environment
Pengcheng Qiu, Chaoyi Wu, Junwei Liu +11
We present a framework for training large language models (LLMs) as diagnostic agents with reinforcement learning, enabling them to manage multi-turn interactive diagnostic process…
End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning
Qiaoyu Zheng, Yuze Sun, Chaoyi Wu +8
The integration of Large Language Models (LLMs) into healthcare is constrained by knowledge limitations, hallucinations, and a disconnect from Evidence-Based Medicine (EBM). While…
ConText: Driving In-context Learning for Text Removal and Segmentation
Fei Zhang, Pei Zhang, Baosong Yang +3
This paper presents the first study on adapting the visual in-context learning (V-ICL) paradigm to optical character recognition tasks, specifically focusing on text removal and se…
Universal Video Temporal Grounding with Generative Multi-modal Large Language Models
Zeqian Li, Shangzhe Di, Zhonghua Zhai +3
This paper presents a computational model for universal video temporal grounding, which accurately localizes temporal moments in videos based on natural language queries (e.g., que…
Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Ziheng Zhao, Lisong Dai, Ya Zhang +2
Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical r…
SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence
Haoning Wu, Xiao Huang, Yaohui Chen +3
Existing evaluations of multimodal large language models (MLLMs) on spatial intelligence are typically fragmented and limited in scope. In this work, we aim to conduct a holistic a…
Multi-Agent System for Comprehensive Soccer Understanding
Jiayuan Rao, Zifeng Li, Haoning Wu +3
Recent advances in soccer understanding have demonstrated rapid progress, yet existing research predominantly focuses on isolated or narrow tasks. To bridge this gap, we propose a…
Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases
Pengcheng Qiu, Chaoyi Wu, Shuyu Liu +7
Recent advancements in reasoning-enhanced large language models (LLMs), such as DeepSeek-R1 and OpenAI-o3, have demonstrated significant progress. However, their application in pro…
M^3Builder: A Multi-Agent System for Automated Machine Learning in Medical Imaging
Jinghao Feng, Qiaoyu Zheng, Chaoyi Wu +4
Agentic AI systems have gained significant attention for their ability to autonomously perform complex tasks. However, their reliance on well-prepared tools limits their applicabil…