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White Matter Fiber-Bundle Analysis Using Non-negative Tensor Factorization.

, , , , and . ICIAR, volume 9730 of Lecture Notes in Computer Science, page 650-657. Springer, (2016)

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Improving QuickBundles to Extract Anatomically Coherent White Matter Fiber-Bundles., , , , and . ICIAR, volume 9730 of Lecture Notes in Computer Science, page 633-641. Springer, (2016)Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis., , , , and . Comput. Methods Programs Biomed., (2021)Graph based neural networks for automatic classification of multiple sclerosis clinical courses., , , and . ESANN, (2018)An automated string-based approach to White Matter fiber-bundles clustering., , , , and . IJCNN, page 1-8. IEEE, (2015)Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity., , , , and . Brain Connect., 12 (5): 476-488 (2022)Blood Vessel Segmentation in Retinal Fundus Images Using Hypercube NeuroEvolution of Augmenting Topologies (HyperNEAT)., , , and . Quantifying and Processing Biomedical and Behavioral Signals, volume 103 of Smart Innovation, Systems and Technologies, Springer, (2019)Optic Disc Detection Using Fine Tuned Convolutional Neural Networks., , , and . SITIS, page 69-75. IEEE Computer Society, (2016)White Matter Fiber-Bundle Analysis Using Non-negative Tensor Factorization., , , , and . ICIAR, volume 9730 of Lecture Notes in Computer Science, page 650-657. Springer, (2016)BioHIPI: Biomedical Hadoop Image Processing Interface., , , and . MOD, volume 10710 of Lecture Notes in Computer Science, page 540-548. Springer, (2017)Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome., , , , , , , and . Frontiers Robotics AI, (2022)