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Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 4th International Wo Alessandro Crimi,Spyridon Bakas,Theo van

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https://doi.org/10.1007/978-3-642-61036-3ning dataset of Brats2018, and 20% of the dataset is regarded as the validation dataset to determine parameters. When the parameters are fixed, we retrain the model on the whole training dataset. The performance achieved on the validation leaderboard is 86%, 77% and 72% Dice scores for the whole tum
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Jaime Prilusky,Eran Hodis,Joel L. Sussmanal of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.
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Johan Fransson,Carl A.K. Borrebaeckto the 3D UNet architecture and augmentation strategy to efficiently handle multimodal MRI input, besides this we introduce approach to enhance segmentation quality with context obtained from models of the same topology operating on downscaled data. We evaluate presented approach on BraTS 2018 datas
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/190323.jpg
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Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries978-3-030-11726-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
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