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- W4384570577 abstract "Segmentation of brain tumors from multiple MRI modalities is necessary for successful disease diagnosis and clinical treatment. In recent years, Transformer-based networks with the self-attention mechanism have been proposed. But they do not show the performance beyond the U-shaped fully convolutional network. In this paper, we apply HFTrans network to the brain tumor segmentation task of BraTS 2022 challenge by focusing on the multi-modalities of MRI with the benefits of Transformer. By applying BraTS-specific modifications of preprocessing, aggressive data augmentation, and postprocessing, our method shows superior results in comparisons between previous best performers. We show that the final result on the BraTS 2022 validation dataset achieves dice scores of 82.94%, 85.48%, and 92.44% and Hausdorff distances of 14.55 mm, 12.96 mm, and 3.77 mm for enhancing tumor, tumor core, and whole tumor, respectively." @default.
- W4384570577 created "2023-07-18" @default.
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- W4384570577 date "2023-01-01" @default.
- W4384570577 modified "2023-10-16" @default.
- W4384570577 title "Multi-modal Transformer for Brain Tumor Segmentation" @default.
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- W4384570577 doi "https://doi.org/10.1007/978-3-031-33842-7_12" @default.
- W4384570577 hasPublicationYear "2023" @default.
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