A STAPLE ensemble of UNet3D, V-Net, and MSA-VNet, fine-tuned from BraTS-GLI to BraTS-SSA, reaches DICE scores around 0.82 to 0.85 on the SSA validation set.
Optimized U-Net for Brain Tumor Segmentation
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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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Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings
A STAPLE ensemble of UNet3D, V-Net, and MSA-VNet, fine-tuned from BraTS-GLI to BraTS-SSA, reaches DICE scores around 0.82 to 0.85 on the SSA validation set.