Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network
This work addresses brain tumor segmentation for medical imaging, presenting an incremental improvement over existing methods.
The paper tackled brain tumor segmentation in MRI by adding an auxiliary classification branch to a deep convolutional neural network, achieving Dice scores of 78.43%, 89.99%, and 84.22% for different tumor regions on the BraTS 2020 validation set.
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.