Semi-supervised learning enhances medical image segmentation by leveraging unlabeled data, reducing reliance on extensive labeled datasets. On the one hand, the distribution discrepancy between limited labeled data and abundant unlabeled data can hinder model generalization. Most existing methods rely on local similarity matching, which may introduce bias. In contrast, Mamba effectively models global context with linear complexity, learning more comprehensive data representations. On the other hand, medical images usually exhibit consistent anatomical structures defined by geometric features. Most existing methods fail to fully utilize global geometric priors, such as volumes, moments etc. In this work, we introduce a global information interaction and geometric priors focus framework (GIGP). Firstly, we present a Global Information Interaction Mamba module to reduce distribution discrepancy between labeled and unlabeled data. Secondly, we propose a Geometric Moment Attention Mechanism to extract richer global geometric features. Finally, we propose Global Geometric Perturbation Consistency to simulate organ dynamics and geometric variations, enhancing the ability of the model to learn generalized features. The superior performance on the NIH Pancreas and Left Atrium datasets demonstrates the effectiveness of our approach.
View on arXiv@article{yu2025_2503.09355,
title={ GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation },
author={ Lianyuan Yu and Xiuzhen Guo and Ji Shi and Hongxiao Wang and Hongwei Li },
journal={arXiv preprint arXiv:2503.09355},
year={ 2025 }
}