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Élagage de réseaux de neurones convolutifs sur graphes pour la sélection de fréquences significatives pour le décodage d'IRMf

Yassine El Ouahidi 1, 2 Hugo Tessier 2, 1 Giulia Lioi 2, 1 Nicolas Farrugia 2, 1 Bastien Pasdeloup 3, 4 Vincent Gripon 1, 2 
1 Lab-STICC_2AI - Equipe Algorithm Architecture Interactions
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
4 Lab-STICC_DECIDE - Equipe DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Abstract : Graph signal processing defines tools to manipulate signals evolving on irregular domains, such as brain signals, by encompassing the spatial dependencies between regions of interest in the brain. In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode Functional Magnetic Resonance Imaging (fMRI) signals. For that, we introduce a deep learning architecture and adapt a pruning methodology to automatically identify such frequencies. Our experiments show that low graph frequencies are consistently identified as the most important for fMRI decoding, with a stronger contribution for the functional graph over the structural one.
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https://hal.archives-ouvertes.fr/hal-03696640
Contributor : Yassine EL OUAHIDI Connect in order to contact the contributor
Submitted on : Thursday, June 16, 2022 - 10:10:12 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM

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  • HAL Id : hal-03696640, version 1

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Yassine El Ouahidi, Hugo Tessier, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, et al.. Élagage de réseaux de neurones convolutifs sur graphes pour la sélection de fréquences significatives pour le décodage d'IRMf. GRETSI 2022, Sep 2022, Nancy, France. ⟨hal-03696640⟩

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