collaborators

7 papers

cs.CV2026

MedUP: Awakening Unified Understanding and Perception in Medical Vision-Language Models

Yuan Wang, Hualiang Wang, Yixin Chen +6

Medical Vision-Language Models (Med-VLMs) excel at verbalizing visual content, yet precise visual perception, segmentation, and grounding remain challenging. Existing approaches ei…

cs.CV2026

MedStreamBench: A Time-Aware Benchmark for Streaming and Proactive Medical Video Understanding

Yuan Wang, Shujian Gao, Songtao Jiang +2

Existing medical video benchmarks primarily evaluate whether a model produces the correct answer, but rarely assess whether it answers at the right time. In real clinical settings,…

cs.CE2026

AtomiMed: Hierarchical Atomic Fact-Checking for Universal Clinical-Aware Medical Report Evaluation

Yuan Wang, Wanxing Chang, Songtao Jiang +8

Traditional metrics for Medical Report Generation (MRG) predominantly rely on surface-level n-gram overlap, which fails to capture clinical factual accuracy and often overlooks cat…

cs.CV2026

BARL: Bilateral Alignment in Representation and Label Spaces for Semi-Supervised Volumetric Medical Image Segmentation

Shujian Gao, Yuan Wang, Zekuan Yu

Semi-supervised medical image segmentation (SSMIS) seeks to match fully supervised performance while sharply reducing annotation cost. Mainstream SSMIS methods rely on \emph{label-…

cs.AI2026

Thinking with Deltas: Incentivizing Reinforcement Learning via Differential Visual Reasoning Policy

Shujian Gao, Yuan Wang, Jiangtao Yan +2

Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced reasoning capabilities in Large Language Models. However, adapting RLVR to multimodal domains suffe…

cs.CE2025

Beyond N-grams: A Hierarchical Reward Learning Framework for Clinically-Aware Medical Report Generation

Yuan Wang, Shujian Gao, Jiaxiang Liu +6

Automatic medical report generation can greatly reduce the workload of doctors, but it is often unreliable for real-world deployment. Current methods can write formally fluent sent…