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Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis.

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Bibliographic Details
Title:	Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis.
Authors:	Cruciani F; Department of Computer Science, University of Verona, Verona, Italy., Brusini L; Department of Computer Science, University of Verona, Verona, Italy., Zucchelli M; Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, Sophia Antipolis, France., Retuci Pinheiro G; MICLab, School of Electrical and Computer Engineering (FEEC), UNICAMP, Campinas, Brazil., Setti F; Department of Computer Science, University of Verona, Verona, Italy., Boscolo Galazzo I; Department of Computer Science, University of Verona, Verona, Italy., Deriche R; Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, Sophia Antipolis, France., Rittner L; MICLab, School of Electrical and Computer Engineering (FEEC), UNICAMP, Campinas, Brazil., Calabrese M; Department of Neurosciences, Biomedicine and Movement, University of Verona, Verona, Italy., Menegaz G; Department of Computer Science, University of Verona, Verona, Italy.
Source:	Journal of neural engineering [J Neural Eng] 2021 Jul 19; Vol. 18 (4). Date of Electronic Publication: 2021 Jul 19.
Publication Type:	Journal Article; Research Support, Non-U.S. Gov't
Journal Info:	Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1741-2552
DOI:	10.1088/1741-2552/ac0f4b