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Prediction of Multiple Sclerosis Patient Disability from Structural Connectivity using Convolutional Neural Networks.

, , , , , , and . EMBC, page 2087-2090. IEEE, (2019)

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Analysis of Cone-Beam Artifacts in off-Centered Circular CT for Four Reconstruction Methods., , and . Int. J. Biomedical Imaging, (2006)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)An automated string-based approach to extracting and characterizing White Matter fiber-bundles., , , , and . Comput. Biol. Medicine, (2016)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)Tensor Based Blind Source Separation in Longitudinal Magnetic Resonance Imaging Analysis., , , and . EMBC, page 3879-3883. IEEE, (2019)Constrained Tensor Decomposition for Longitudinal Analysis of Diffusion Imaging Data., , , and . IEEE J. Biomed. Health Informatics, 24 (4): 1137-1148 (2020)A Model-Guided String-Based Approach to White Matter Fiber-Bundles Extraction., , , , , and . BIH, volume 9250 of Lecture Notes in Computer Science, page 135-144. Springer, (2015)An automated string-based approach to White Matter fiber-bundles clustering., , , , and . IJCNN, page 1-8. IEEE, (2015)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)