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

Y. E. Ouahidi, H. Tessier, G. Lioi, N. Farrugia, B. Pasdeloup and V. Gripon, "Élagage de réseaux de neurones convolutifs sur graphes pour la sélection de fréquences significatives pour le décodage d'IRMf," in GRETSI, 2022.

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.

Download manuscript.

Bibtex
@inproceedings{OuaTesLioFarPasGri2022,
  author = {Yassine El Ouahidi and Hugo Tessier and
Giulia Lioi and Nicolas Farrugia and Bastien Pasdeloup
and Vincent Gripon },
  title = {Élagage de réseaux de neurones
convolutifs sur graphes pour la sélection de
fréquences significatives pour le décodage d'IRMf},
  booktitle = {GRETSI},
  year = {2022},
}




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