activity
20242026
collaborators

11 papers

cs.CV2026

MIRA: Medical Image Reflection for Agentic Diagnosis

Shengzhi Wang, Jun Yang, Kai Wu +11

Medical visual agents can use tools to inspect images and retrieve external knowledge, but indiscriminate tool use may introduce noisy or misleading evidence. Reliable diagnosis th…

cs.CV2026

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases

Qi Chen, Wenxuan Li, Pedro R. A. S. Bassi +14

Artificial intelligence (AI) has achieved remarkable success in medical imaging, but it is widely recognized that these models often perform inconsistently across real-world clinic…

eess.IV2026

SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

Jia Fu, Litingyu Wang, He Li +27

Accurate delineation of Gross Tumor Volume (GTV), Lymph Node Clinical Target Volume (LN CTV), and Organ-at-Risk (OAR) from Computed Tomography (CT) scans is essential for precise r…

cs.CV2025

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation

Linhao Li, Yiwen Ye, Ziyang Chen +1

3D medical image segmentation often faces heavy resource and time consumption, limiting its scalability and rapid deployment in clinical environments. Existing efficient segmentati…

cs.CV2025

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation

Yiwen Ye, Yicheng Wu, Xiangde Luo +5

Foundation models have become a promising paradigm for advancing medical image analysis, particularly for segmentation tasks where downstream applications often emerge sequentially…

cs.CV2025

From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels

Zhisong Wang, Yiwen Ye, Ziyang Chen +3

Scribble-based weakly supervised segmentation methods have shown promising results in medical image segmentation, significantly reducing annotation costs. However, existing approac…