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Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement, , , , , , , , and . (2019)cite arxiv:1907.13418.Generic acquisition protocol for quantitative MRI of the spinal cord, , , , , , , , , and 81 other author(s). Nature Protocols, 16 (10): 4611-4632 (August 2021)Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset., , , , , , , , , and 2 other author(s). Frontiers Neuroinformatics, (March 2023)Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy., , , , , , , , , and . NeuroImage, (2020)Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI., , , , , , , , and . NeuroImage, (2021)Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising., , , , , , , , , and . NeuroImage, (2020)Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers, , , , , , , , , and 81 other author(s). Scientific Data, (August 2021)Spinal cord grey matter segmentation challenge., , , , , , , , , and 18 other author(s). NeuroImage, (2017)Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results., , , , , , , , , and 9 other author(s). NeuroImage, (2020)Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo., , , , and . NeuroImage, (2015)