33 citations · 81 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment
Dandan Shan, Zihan Li, Wentao Chen +3
Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19. However, there are few relevant studies due to the lack of detailed informatio…
TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
Zihan Li, Dihan Li, Cangbai Xu +4
Medical image segmentation is one of the most fundamental tasks concerning medical information analysis. Various solutions have been proposed so far, including many deep learning-b…