Deep Mutual Distillation for Semi-supervised Medical Image Segmentation

In this paper, we focus on semi-supervised medical image segmentation. Consistency regularization methods such as initialization perturbation on two networks combined with entropy minimization are widely used to deal with the task. However, entropy minimization-based methods force networks to agree on all parts of the training data. For extremely ambiguous regions, which are common in medical images, such agreement may be meaningless and unreliable. To this end, we present a conceptually simple yet effective method, termed Deep Mutual Distillation (DMD), a high-entropy online mutual distillation process, which is more informative than a low-entropy sharpened process, leading to more accurate segmentation results on ambiguous regions, especially the outer branches. Furthermore, to handle the class imbalance and background noise problem, and learn a more reliable consistency between the two networks, we exploit the Dice loss to supervise the mutual distillation. Extensive comparisons with all state-of-the-art on LA and ACDC datasets show the superiority of our proposed DMD, reporting a significant improvement of up to 1.15% in terms of Dice score when only 10% of training data are labelled in LA. We compare DMD with other consistency-based methods with different entropy guidance to support our assumption. Extensive ablation studies on the chosen temperature and loss function further verify the effectiveness of our design. The code is publicly available at https://github.com/SilenceMonk/Dual-Mutual-Distillation.