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Benefits of Linear Conditioning for Segmentation using Metadata., , , , , и . MIDL, том 143 из Proceedings of Machine Learning Research, стр. 416-430. PMLR, (2021)Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis., , , , , , , и . CoRR, (2024)Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study., , , , , , , , , и 15 other автор(ы). NeuroImage, (2017)Primary Categorizing and Masking Cerebral Small Vessel Disease Based on "Deep Learning System"., , , , , , , , , и . Frontiers Neuroinformatics, (2020)Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations., , , , и . CoRR, (2023)One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation., , , , и . MICCAI (1), том 13431 из Lecture Notes in Computer Science, стр. 133-142. Springer, (2022)Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers, , , , , , , , , и 81 other автор(ы). Scientific Data, (августа 2021)A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts., , , , , , , , , и 1 other автор(ы). CoRR, (2024)Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks., , , , , , , , , и 42 other автор(ы). CoRR, (2018)Generic acquisition protocol for quantitative MRI of the spinal cord, , , , , , , , , и 81 other автор(ы). Nature Protocols, 16 (10): 4611-4632 (августа 2021)