3 papers
cs.CV2026
H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentation
Jia Wang, Jiaming Cai, Zunying Hu +4
Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through registration. However, existin…
cs.CV2025
Label-efficient multi-organ segmentation with a diffusion model
Yongzhi Huang, Fengjun Xi, Liyun Tu +7
Accurate segmentation of multiple organs in Computed Tomography (CT) images plays a vital role in computer-aided diagnosis systems. While various supervised learning approaches hav…
eess.IV2025
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation
Feng Zhou, Yanjie Zhou, Longjie Wang +3
Traditional one-shot medical image segmentation (MIS) methods use registration networks to propagate labels from a reference atlas or rely on comprehensive sampling strategies to g…