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H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task
In this paper, we propose a Hybrid High-resolution and Non-local Feature...
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No New-Net
In this paper we demonstrate the effectiveness of a well trained U-Net i...
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Robustness of Brain Tumor Segmentation
We address the generalization behavior of deep neural networks in the co...
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An attempt at beating the 3D U-Net
The U-Net is arguably the most successful segmentation architecture in t...
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Top 10 BraTS 2020 challenge solution: Brain tumor segmentation with self-ensembled, deeply-supervised 3D-Unet like neural networks
Brain tumor segmentation is a critical task for patient's disease manage...
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Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation
U-Net has achieved huge success in various medical image segmentation ch...
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Does contextual information improve 3D U-Net based brain tumor segmentation?
Effective, robust and automatic tools for brain tumor segmentation are n...
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nnU-Net for Brain Tumor Segmentation
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline we are able to improve its segmentation performance substantially. We furthermore re-implement the BraTS ranking scheme to determine which of our nnU-Net variants best fits the requirements imposed by it. Our final ensemble took the first place in the BraTS 2020 competition with Dice scores of 88.95, 85.06 and 82.03 and HD95 values of 8.498,17.337 and 17.805 for whole tumor, tumor core and enhancing tumor, respectively.
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