33 citations · 81 across the 16 of their papers we have counts for
6 papers · 1 filter
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…