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Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease., , , , and . CoRR, (2021)Gradient-Based Reliability Maps for ACM-Based Segmentation of Hippocampus., , , and . IEEE Trans. Biomed. Eng., 61 (4): 1015-1026 (2014)Hippocampus segmentation through gradient based reliability maps for local blending of ACM energy terms., , , and . ISBI, page 53-56. IEEE, (2013)Novel approaches for medication compliance and effectiveness analysis and support in cardiovascular disease patients., , , , , and . EMBC, page 888-891. IEEE, (2011)Hippocampus segmentation by optimizing the local contribution of image and prior terms, through graph cuts and multi-atlas., , , and . ISBI, page 1168-1171. IEEE, (2012)Machine and deep learning for longitudinal biomedical data: a review of methods and applications., , , , , , , , and . Artif. Intell. Rev., 56 (S2): 1711-1771 (November 2023)A new scheme for the assessment of the robustness of Explainable Methods Applied to Brain Age estimation., , , , , , , and . CBMS, page 492-497. IEEE, (2021)Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge., , , , , , , , , and 30 other author(s). IEEE J. Biomed. Health Informatics, 27 (7): 3302-3313 (July 2023)Stacked BCDU-Net with Semantic CMR Synthesis: Application to Myocardial Pathology Segmentation Challenge., , , , , and . MyoPS@MICCAI, volume 12554 of Lecture Notes in Computer Science, page 1-16. Springer, (2020)Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge., , , , , , , , , and 37 other author(s). IEEE Trans. Medical Imaging, 40 (12): 3543-3554 (2021)