Deep mutual learning for brain tumor segmentation with the fusion network

Huan Gao, Qiguang Miao, Daikai Ma, Ruyi Liu

Abstract: Highlights•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.

External IDs:dblp:journals/ijon/GaoMML23