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• W3027166080 abstract "A cascade of global context convolutional neural networks is proposed to segment multi-modality MR images with brain tumor into three subregions: enhancing tumor, whole tumor and tumor core. Each network is a modification of the 3D U-Net consisting of residual connection, group normalization and deep supervision. In addition, we apply Global Context (GC) block to capture long-range dependency and inter-channel dependency. We use a combination of logarithmic Dice loss and weighted cross entropy loss to focus on less accurate voxels and improve the accuracy. Experiments with BraTS 2019 validation set show the proposed method achieved average Dice scores of 0.77338, 0.90712, 0.83911 for enhancing tumor, whole tumor and tumor core, respectively. The corresponding values for BraTS 2019 testing set were 0.79303, 0.87962, 0.82887 for enhancing tumor, whole tumor and tumor core, respectively." @default.
• W3027166080 created "2020-05-29" @default.
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• W3027166080 date "2020-01-01" @default.
• W3027166080 modified "2023-09-28" @default.
• W3027166080 title "Cascaded Global Context Convolutional Neural Network for Brain Tumor Segmentation" @default.
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• W3027166080 doi "https://doi.org/10.1007/978-3-030-46640-4_30" @default.
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