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 methods encounter challenges in
accurately predicting labels for the unlabeled data, resulting in disruptive
noise during training and susceptibility to erroneous information overfitting.
Additionally, applying perturbations to inaccurate predictions further reduces
consistent learning. To address these concerns, a novel \textbf{C}ross-head
\textbf{m}utual \textbf{m}ean-\textbf{t}eaching Network (CMMT-Net) is proposed
to address these issues. The CMMT-Net comprises teacher-student networks and
incorporates strong-weak data augmentation within a shared encoder,
facilitating cross-head co-training by capitalizing on both self-training and
consistent learning. The consistent learning is enhanced by averaging teacher
networks and mutual virtual adversarial training, leading to deterministic and
higher-quality predictions. The diversity of consistency training samples can
be enhanced through the use of Cross-Set CutMix, which also helps mitigate
issues related to distribution mismatch. Notably, CMMT-Net simultaneously
implements data-level, feature-level, and network-level perturbations, boosting
model diversity and generalization performance. The proposed method
consistently outperforms existing SSMIS methods on three publicly available
datasets across various semi-supervised settings. Code and logs will be
available at https://github.com/Leesoon1984/CMMT-Net.

用 CMMT-Net 方法解决半监督医学图像分割中的标签预测准确性、噪声以及信息过度拟合等问题，并在公开数据集上显著优于现有方法。