Deep mutual learning for brain tumor segmentation with the fusion network.

• This paper introduces the mutual learning strategy train the brain tumor segmentation network, using the shallowest feature map to supervise the subsequent feature map of the network. using the deepest logits to supervise the previous shallow network's logits. The shallow feature map and deep logit supervise mutually and improve the accuracy of tumor sub-region segmentation. • This paper introduces the depth supervision to train this network, using the prediction of each up-sample layer is to deep supervise the training process for enlarging the receptive field to improve the overall segmentation accuracy. • A large number of experiments on BraTS dataset show that our method can effectively improve the accuracy of brain tumor segmentation and achieve the performance of SOTA. Deep learning methods have been successfully applied to Brain tumor segmentation. However, the extreme data imbalance exists in the different sub-regions of tumor, results in training the deep learning methods on these data will reduce the accuracy of segmentation. We introduce the deep mutual learning strategy to address the challenges, the proposed integrates transformer layers in both encoder and decoder of a U-Net architecture. In the network, using the prediction of up-sampled layer is to deep supervise the training process for enlarging the receptive field to extract features, the feature map of the shallowest layer supervises the subsequent feature map of layers to keep more edge information to guide the sub-region segmentation accuracy. the classification logits of the deepest layer supervise the previous layer of logits to get more semantic information for distinguish of tumor sub-regions. Furthermore, the feature map and the classification logits supervise mutually to improve the overall segmentation accuracy. The experimental results on benchmark dataset shows that our method has significant performance gain over existing methods. [ABSTRACT FROM AUTHOR]