96 citations · 177 across the 10 of their papers we have counts for
5 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…
3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR)
Hua-Chieh Shao, Tielige Mengke, Jie Deng +1
The reconstruction of 3D cine-MRI is challenged by highly undersampled k-space data in each cine frame, due to the slow speed of MR signal acquisition. We proposed a machine learni…
SAMScore: A Content Structural Similarity Metric for Image Translation Evaluation
Yunxiang Li, Meixu Chen, Kai Wang +3
Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness. The…
Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
Yunxiang Li, Hua-Chieh Shao, Xiao Liang +5
Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing di…
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…