  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.
