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Why is the Winner the Best?

, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and . CVPR, page 19955-19966. IEEE, (2023)

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Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss., , , , , , , , , and . CoRR, (2019)SegMatch: A semi-supervised learning method for surgical instrument segmentation., , , , , and . CoRR, (2023)Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets., , , , , , , and . CoRR, (2020)Why is the Winner the Best?, , , , , , , , , and 115 other author(s). CVPR, page 19955-19966. IEEE, (2023)Label merge-and-split: A graph-colouring approach for memory-efficient brain parcellation., , , and . CoRR, (2024)Why is the winner the best?, , , , , , , , , and 41 other author(s). CoRR, (2023)Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI., , , , , , , , and . Int. J. Comput. Assist. Radiol. Surg., 15 (9): 1445-1455 (2020)Permutohedral Attention Module for Efficient Non-local Neural Networks., , , , , and . MICCAI (6), volume 11769 of Lecture Notes in Computer Science, page 393-401. Springer, (2019)Driving Points Prediction for Abdominal Probabilistic Registration., , , , and . MLMI@MICCAI, volume 13583 of Lecture Notes in Computer Science, page 288-297. Springer, (2022)CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation., , , , , , , , , and 30 other author(s). Medical Image Anal., (2023)