Low-Rank Convolutional Networks for Brain Tumor Segmentation

The automated segmentation of brain tumors is crucial for various clinical purposes from diagnosis to treatment planning to follow-up evaluations. The vast majority of effective models for tumor segmentation are based on convolutional neural networks with millions of parameters being trained. Such complex models can be highly prone to overfitting especially in cases where the amount of training data is insufficient. In this work, we devise a 3D U-Net-style architecture with residual blocks, in which low-rank constraints are imposed on weights of the convolutional layers in order to reduce overfitting. Within the same architecture, this helps to design networks with several times fewer parameters. We investigate the effectiveness of the proposed technique on the BraTS 2020 challenge.