GapMatch: Bridging Instance and Model Perturbations for Enhanced Semi-Supervised Medical Image Segmentation

Medical image segmentation provides detailed understanding and aids in diagnosis, treatment planning, and monitoring of diseases. Due to the high cost of acquiring labeled data in the field of medical image analysis, semi-supervised segmentation methods have garnered increasing attention. Benefiting from their simplicity and effectiveness, consistency regularization-based methods have emerged as a significant research focus by utilizing perturbations. However, existing methods typically consider perturbation strategies from only a single perspective: either instance perturbation or model perturbation, thus ignoring the potential benefit of effectively combining both. In response, we propose a unified perturbation framework named GapMatch, which bridges instance and model perturbations to broaden the perturbation space and employs dual perturbation to impose consistency regularization on the model. Specifically, GapMatch involves using instance perturbation to update the decision boundary and model perturbation to further optimize it. These two steps mutually reinforce each other in an iterative manner, effectively pushing the decision boundary towards low-density regions while maximizing the class margin. Extensive experimental results on two popular medical image benchmarks demonstrate the effectiveness and generality of the proposed method.