Optimized U-Net for Brain Tumor Segmentation

Code

• github.com/NVIDIA/DeepLearningExamples
  OfficialIn papertf★ 14,750
• github.com/EverLookNeverSee/Optimized-U-Net
  tf★ 5

Abstract

We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge. To find the optimal model architecture and the learning schedule, we have run an extensive ablation study to test: deep supervision loss, Focal loss, decoder attention, drop block, and residual connections. Additionally, we have searched for the optimal depth of the U-Net encoder, number of convolutional channels and post-processing strategy. Our method won the validation phase and took third place in the test phase. We have open-sourced the code to reproduce our BraTS21 submission at the NVIDIA Deep Learning Examples GitHub Repository.

Tasks

Reproductions