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ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.

, , , , , , , , , , , , and . NeuroImage, (September 2023)

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Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study., , , , , , , , , and 10 other author(s). NeuroImage, (2021)ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI., , , , , , , , , and 3 other author(s). NeuroImage, (September 2023)Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach., , , , , , , , , and 8 other author(s). Frontiers Neuroinformatics, (2021)Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs., , , , , , , , , and 11 other author(s). Neuroinformatics, 19 (4): 597-618 (2021)Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity., , , , , , , , , and 25 other author(s). Int. J. Biomed. Imaging, (2022)ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI., , , , , , , , , and 3 other author(s). CoRR, (2022)