Mask-Guided Proxy Mining Network for Few-Shot Medical Image Segmentation

作者：Wendong Huang, Jinwu Hu, Y. Wang, Xiuli Bi, Yucheng Shu, Xuezong Yang, Yanliang Zhang, Bin Xiao · 发表于：IEEE Transactions on Image Processing · 年份：2026 · DOI：10.1109/tip.2026.3684405 · 被引用次数：5 · 研究领域：Advanced Neural Network Applications、Medical Image Segmentation Techniques、Retinal Imaging and Analysis

Few-shot medical image segmentation (FSMIS) has attracted increasing attention as a promising technique for solving medical image segmentation tasks by relying on only a small amount of labeled data from new classes. Current FSMIS methods typically employ pixel-level semantic correlations between support-query image pairs to guide the segmentation of query images. However, the class information gap between support and query images may induce severe mismatches, leading to semantic ambiguity between foreground and background pixels. To address this issue, we propose a novel mask-guided proxy mining network (MPMNet), which mines a set of representative reference features (termed proxies) from support and query images to rectify foreground-background ambiguity. Specifically, to eliminate false pairwise matches caused by excessive intra-class variations, we design a mask-guided proxy mining module to adaptively learn representative proxies that can perceive visual differences between objects with different scales and shapes. Moreover, we integrate a hierarchical prior generation module and a context-aware feature enrichment module into MPMNet to obtain multi-scale information and enhance the discriminability of features. With these well-designed components and structures, our MPMNet can effectively overcome the adverse effects of false pixel matches by establishing proxy-level semantic correlations. Extensive experiments on three standard medical segmentation benchmarks demonstrat...