1 citations · 2 across the 8 of their papers we have counts for
8 papers
From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
Yazhou Zhu
Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defining evidence before inferenc…
PSP: Harnessing Position and Shape Priors for Cross-Domain Few-Shot Medical Image Segmentation
Bin Xu, Yazhou Zhu, Haofeng Zhang
Few-Shot Medical Image Segmentation (FSMIS) offers a powerful solution to data scarcity but struggles to generalize across different imaging modalities. This performance collapse s…
Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Prompting
Yuntian Bo, Yazhou Zhu, Piotr Koniusz +1
Conventional few-shot medical image segmentation (FSMIS) approaches face performance bottlenecks that hinder broader clinical applicability. Although the Segment Anything Model (SA…
MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentation
Yazhou Zhu, Haofeng Zhang
Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) is a potential solution for segmenting medical images with limited annotation using knowledge from other domains. The si…
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image Segmentation
Yuntian Bo, Yazhou Zhu, Lunbo Li +1
Existing few-shot medical image segmentation (FSMIS) models fail to address a practical issue in medical imaging: the domain shift caused by different imaging techniques, which lim…
RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation
Yazhou Zhu, Minxian Li, Qiaolin Ye +3
Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current…