33 citations · 81 across the 16 of their papers we have counts for
16 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…
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…
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…
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…
ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation
Zihan Li, Yuan Zheng, Dandan Shan +6
Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally ca…
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
Yunxiang Li, Bowen Jing, Zihan Li +2
Automatic segmentation of medical images is crucial in modern clinical workflows. The Segment Anything Model (SAM) has emerged as a versatile tool for image segmentation without sp…
ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding
Zihan Li, Yuan Zheng, Xiangde Luo +2
Medical image segmentation plays a critical role in clinical decision-making, treatment planning, and disease monitoring. However, accurate segmentation of medical images is challe…
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
Yiqing Wang, Zihan Li, Jieru Mei +7
Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a n…
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge
Yunxiang Li, Zihan Li, Kai Zhang +3
The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specializ…
Implicit Neural Representation for Cooperative Low-light Image Enhancement
Shuzhou Yang, Moxuan Ding, Yanmin Wu +2
The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-fa…
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…
LViT: Language meets Vision Transformer in Medical Image Segmentation
Zihan Li, Yunxiang Li, Qingde Li +6
Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the c…
Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency
Zihan Li, Wentao Chen, Zhiqing Wei +2
Supervised learning has been widely used for attack categorization, requiring high-quality data and labels. However, the data is often imbalanced and it is difficult to obtain suff…