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arXiv · 2110.03352

Optimized U-Net for Brain Tumor Segmentation

Michał Futrega·Alexandre Milesi·Michal Marcinkiewicz·Pablo Ribalta
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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.

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Michał Futrega, Alexandre Milesi, Michal Marcinkiewicz, Pablo Ribalta. 2021-10-07. Optimized U-Net for Brain Tumor Segmentation. https://arxiv.org/abs/2110.03352

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