  Brain tumor segmentation plays an essential role in medical image analysis.
In recent studies, deep convolution neural networks (DCNNs) are extremely
powerful to tackle tumor segmentation tasks. We propose in this paper a novel
training method that enhances the segmentation results by adding an additional
classification branch to the network. The whole network was trained end-to-end
on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training
dataset. On the BraTS's validation set, it achieved an average Dice score of
78.43%, 89.99%, and 84.22% respectively for the enhancing tumor, the whole
tumor, and the tumor core.
