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