Explainable 3D-CNN for Multiple Sclerosis patients stratification - INRIA - Institut National de Recherche en Informatique et en Automatique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Explainable 3D-CNN for Multiple Sclerosis patients stratification

Résumé

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.

Mots clés

Fichier non déposé

Dates et versions

hal-02971361 , version 1 (19-10-2020)

Identifiants

Citer

Federica Cruciani, Lorenza Brusini, Mauro Zucchelli, Gustavo Retuci Pinheiro, Francesco Setti, et al.. Explainable 3D-CNN for Multiple Sclerosis patients stratification. ICPR 2020 - 25th International Conference on Pattern Recognition Workshops and Challenges, Jan 2021, Milan, Italy. ⟨10.1007/978-3-030-68796-0_8⟩. ⟨hal-02971361⟩
168 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More