2 citations · 2 across the 3 of their papers we have counts for
7 papers
OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis
Zihan Li, Feiyang Liu, Dandan Shan +2
Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distribution shifts across scanners, protocols, and patient population…
Boosting Medical Visual Understanding From Multi-Granular Language Learning
Zihan Li, Yiqing Wang, Sina Farsiu +1
Recent advances in image-text pretraining have significantly enhanced visual understanding by aligning visual and textual representations. Contrastive Language-Image Pretraining (C…
Scale-aware Adaptive Supervised Network with Limited Medical Annotations
Zihan Li, Dandan Shan, Yunxiang Li +2
Medical image segmentation faces critical challenges in semi-supervised learning scenarios due to severe annotation scarcity requiring expert radiological knowledge, significant in…
VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge
Zihan Li, Diping Song, Zefeng Yang +5
The need for improved diagnostic methods in ophthalmology is acute, especially in the underdeveloped regions with limited access to specialists and advanced equipment. Therefore, w…
STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation
Dandan Shan, Zihan Li, Yunxiang Li +3
Accurate segmentation of lesions plays a critical role in medical image analysis and diagnosis. Traditional segmentation approaches that rely solely on visual features often strugg…
Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases
Xiangde Luo, Zihan Li, Shaoting Zhang +2
Deep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and…