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20242026
most citedEHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis

1 citations · 1 across the 3 of their papers we have counts for

collaborators

10 papers

cs.CV2026

PhenoLIP: Integrating Phenotype Ontology Knowledge into Medical Vision-Language Pretraining

Cheng Liang, Chaoyi Wu, Weike Zhao +3

Recent progress in large-scale CLIP-like vision-language models(VLMs) has greatly advanced medical image analysis. However, most existing medical VLMs still rely on coarse image-te…

cs.CL20251 cited

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…

cs.CV2025

Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping

Dexuan He, Xiao Zhou, Wenbin Guan +11

Rare cancers comprise 20-25% of all malignancies but face major diagnostic challenges due to limited expert availability-especially in pediatric oncology, where they represent over…

cs.CV2025

SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass

Yanxu Meng, Haoning Wu, Ya Zhang +1

3D content generation has recently attracted significant research interest, driven by its critical applications in VR/AR and embodied AI. In this work, we tackle the challenging ta…

cs.CL2025

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…

cs.CV2025

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…