文献类型：期刊文献

英文题名：Advancing Medical Image Segmentation via Self-supervised Instance-adaptive Prototype Learning

作者：Liang, Guoyan[1]; Zhou, Qin[2]; Chen, Jingyuan[1]; Wang, Zhe[2]; Yao, Chang[1]

机构：[1] Zhejiang University, Hangzhou, China; [2] East China University of Science and Technology, Shanghai, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250339939)

语种：英文

外文关键词：Decoding - Learning systems - Medical image processing - Pixels - Self-supervised learning - Semantics - Supervised learning

摘要：Medical Image Segmentation (MIS) plays a crucial role in medical therapy planning and robot navigation. Prototype learning methods in MIS focus on generating segmentation masks through pixel-to-prototype comparison. However, current approaches often overlook sample diversity by using a fixed prototype per semantic class and neglect intra-class variation within each input. In this paper, we propose to generate instance-adaptive prototypes for MIS, which integrates a common prototype proposal (CPP) capturing common visual patterns and an instance-specific prototype proposal (IPP) tailored to each input. To further account for the intra-class variation, we propose to guide the IPP generation by re-weighting the intermediate feature map according to their confidence scores. These confidence scores are hierarchically generated using a transformer decoder. Additionally we introduce a novel self-supervised filtering strategy to prioritize the foreground pixels during the training of the transformer decoder. Extensive experiments demonstrate favorable performance of our method. Copyright ? 2025, The Authors. All rights reserved.