InterTeach: A Novel Approach for Semi-Supervised Medical Image Segmentation Using Cooperative Teacher-Student Networks

In medical image segmentation, the reliance on extensive, high-quality labeled datasets poses a significant challenge, especially considering the associated costs and the requirement for specialized expertise. In response, the field has progressively embraced semi-supervised learning (SSL) methods that leverage both labeled and unlabeled data. Nonetheless, these methods frequently encounter issues related to inconsistent label quality and constrained generalizability of models. To surmount these obstacles, we present InterTeach, an innovative SSL framework that seamlessly integrates cross-supervision with the mean teacher model. This framework facilitates effective knowledge transfer and boosts model performance through the implementation of two unique teacher-student training configurations. Herein, knowledge is exchanged between models via their respective teacher counterparts, facilitating mutual learning and enhancement. This strategy diverges from traditional SSL approaches, which mainly depend on mutual learning between two models updated through gradient descent. Furthermore, the incorporation of Feature Divergence Loss (FDL) in InterTeach encourages the transfer of diverse and complementary knowledge between models, thereby enriching the overall learning dynamics. The evaluation results revealed that our method could approach or even match the performance of fully supervised learning methods on certain evaluation metrics. This finding further confirms the effectiveness and wide applicability of the IntraTeach method in handling multi-modal and multi-dimensional medical image segmentation tasks.