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A Graph Based Classification Method for Multiple Sclerosis Clinical Forms Using Support Vector Machine., , , , and . MLMMI@ICML, volume 9487 of Lecture Notes in Computer Science, page 57-64. Springer, (2015)Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis., , , , and . Comput. Methods Programs Biomed., (2021)Implementation of an Absolute Brain $^1$H-MRS Quantification Method to Assess Different Tissue Alterations in Multiple Sclerosis., , , , , , and . IEEE Trans. Biomed. Eng., 59 (10): 2687-2694 (2012)Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity., , , , and . Brain Connect., 12 (5): 476-488 (2022)Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome., , , , , , , and . Frontiers Robotics AI, (2022)A More Interpretable Classifier For Multiple Sclerosis., , , , and . ISBI, page 1062-1066. IEEE, (2021)A Comparison of Machine Learning Approaches for Classifying Multiple Sclerosis Courses Using MRSI and Brain Segmentations., , , , , , and . ICANN (2), volume 10614 of Lecture Notes in Computer Science, page 643-651. Springer, (2017)A Weakly Supervised Gradient Attribution Constraint for Interpretable Classification and Anomaly Detection., , , , and . IEEE Trans. Medical Imaging, 42 (11): 3336-3347 (November 2023)A longitudinal model for variations detection in white matter fiber-bundles., , , , , , , and . IWSSIP, page 57-60. IEEE, (2015)A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders., , , , , , , and . CoRR, (2019)