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Explainable 3D-CNN for Multiple Sclerosis patients stratification

Authors :
Francesco Setti
Mauro Zucchelli
Gloria Menegaz
Gustavo Retuci Pinheiro
Rachid Deriche
Massimiliano Calabrese
Lorenza Brusini
Ilaria Boscolo Galazzo
Federica Cruciani
Leticia Rittner
Università degli studi di Verona = University of Verona (UNIVR)
COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)
Computational Imaging of the Central Nervous System (ATHENA)
Inria Sophia Antipolis - Méditerranée (CRISAM)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Universidade Estadual de Campinas = University of Campinas (UNICAMP)
ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
European Project: 694665,H2020 ERC,ERC-2015-AdG,CoBCoM(2016)
University of Verona (UNIVR)
Universidade Estadual de Campinas (UNICAMP)
Source :
ICPR 2020-25th International Conference on Pattern Recognition Workshops and Challenges, ICPR 2020-25th International Conference on Pattern Recognition Workshops and Challenges, Jan 2021, Milan, Italy. ⟨10.1007/978-3-030-68796-0_8⟩, Pattern Recognition. ICPR International Workshops and Challenges, Pattern Recognition. ICPR International Workshops and Challenges, Jan 2021, Milan, Italy, Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687953, ICPR Workshops (3), Pattern Recognition. ICPR International Workshops and Challenges-Virtual Event, January 10–15, 2021, Proceedings, Part III
Publication Year :
2021
Publisher :
HAL CCSD, 2021.

Abstract

International audience; The growing availability of novel interpretation techniques opened the way to the application of deep learning models in the clinical field, including neuroimaging, where their use is still largely underexploited. In this framework, we focus the stratification of Multiple Sclerosis (MS) patients in the Primary Progressive versus the Relapsing-Remitting state of the disease using a 3D Convolutional Neural Network trained on structural MRI data. Within this task, the application of Layer-wise Relevance Propagation visualization allowed detecting the voxels of the input data mostly involved in the classification decision, potentially bringing to light brain regions which might reveal disease state.

Details

Language :
English
ISBN :
978-3-030-68795-3
ISBNs :
9783030687953
Database :
OpenAIRE
Journal :
ICPR 2020-25th International Conference on Pattern Recognition Workshops and Challenges, ICPR 2020-25th International Conference on Pattern Recognition Workshops and Challenges, Jan 2021, Milan, Italy. ⟨10.1007/978-3-030-68796-0_8⟩, Pattern Recognition. ICPR International Workshops and Challenges, Pattern Recognition. ICPR International Workshops and Challenges, Jan 2021, Milan, Italy, Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687953, ICPR Workshops (3), Pattern Recognition. ICPR International Workshops and Challenges-Virtual Event, January 10–15, 2021, Proceedings, Part III
Accession number :
edsair.doi.dedup.....55e9eabb4b3e6a72590e8a40ad67785b