Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation

TL;DR

This paper introduces CMMT-Net, a novel semi-supervised medical image segmentation framework that uses mutual mean-teaching, strong-weak augmentation, and adversarial training to improve label prediction accuracy and model robustness.

Contribution

The paper proposes a new CMMT-Net architecture with mutual supervision, virtual adversarial training, and Cross-Set CutMix to enhance semi-supervised segmentation performance.

Findings

Significant improvements over SOTA on three datasets.

Effective handling of label noise and distribution mismatch.

Enhanced model generalization through multi-level perturbations.

Abstract

Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Nevertheless, existing state-of-the-art (SOTA) methods encounter challenges in accurately predicting labels for the unlabeled data, giving rise to disruptive noise during training and susceptibility to erroneous information overfitting. Moreover, applying perturbations to inaccurate predictions further reduces consistent learning. To address these concerns, we propose a novel Cross-head mutual mean-teaching Network (CMMT-Net) incorporated strong-weak data augmentation, thereby benefitting both self-training and consistency learning. Specifically, our CMMT-Net consists of both teacher-student peer networks with a share encoder and dual slightly different decoders, and the pseudo labels generated by one mean teacher head are adopted to supervise the other student branch to achieve a mutual consistency. Furthermore, we propose mutual virtual adversarial training (MVAT) to smooth the decision boundary and enhance feature representations. To diversify the consistency training samples, we employ Cross-Set CutMix strategy, which also helps address distribution mismatch issues. Notably, CMMT-Net simultaneously implements data, feature, and network perturbations, amplifying model diversity and generalization performance. Experimental results on three publicly available datasets indicate that our approach yields remarkable improvements over previous SOTA methods across various semi-supervised scenarios. Code and logs will be available at https://github.com/Leesoon1984/CMMT-Net.