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Tensor Factorization of Brain Structural Graph for Unsupervised Classification in Multiple Sclerosis

Abstract : Analysis of longitudinal changes in brain diseases is essential for a better characterization of pathological processes and evaluation of the prognosis. This is particularly important in Multiple Sclerosis (MS) which is the first traumatic disease in young adults, with unknown etiology and characterized by complex inflammatory and degenerative processes leading to different clinical courses. In this work, we propose a fully automated tensor-based algorithm for the classification of MS clinical forms based on the structural connectivity graph of the white matter (WM) network. Using non-negative tensor factorization (NTF), we first focused on the detection of pathological patterns of the brain WM network affected by significant longitudinal variations. Second, we performed unsupervised classification of different MS phenotypes based on these longitudinal patterns, and finally, we used the latent factors obtained by the factorization algorithm to identify the most affected brain regions.
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https://hal.archives-ouvertes.fr/hal-03241651
Contributor : Berardino Barile <>
Submitted on : Friday, May 28, 2021 - 7:31:04 PM
Last modification on : Monday, June 14, 2021 - 4:59:27 PM

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Beradino Barile, Aldo Marzullo, Claudio Stamile, Françoise Durand-Dubief, Dominique Sappey-Marinier. Tensor Factorization of Brain Structural Graph for Unsupervised Classification in Multiple Sclerosis. 2020 25th International Conference on Pattern Recognition (ICPR), Jan 2021, Milan (virtual), Italy. pp.5052-5059, ⟨10.1109/ICPR48806.2021.9412491⟩. ⟨hal-03241651⟩

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