Geometric deep learning : graph convolution for population based Autism prediction
FR |
EN
Auteur / Autrice : | Sirine Sboui |
Direction : | Faten Chaieb-Chakchouk |
Type : | Projet de thèse |
Discipline(s) : | Informatique |
Date : | Inscription en doctorat le 13/01/2023 |
Etablissement(s) : | Université Paris-Panthéon-Assas |
Ecole(s) doctorale(s) : | École doctorale des sciences économiques et gestion, sciences de l'information et de la communication (Paris) |
Résumé
FR
Geometric deep learning specifically deep leaning on graphs has been shown a great results in several domains (natural language processing , medical, video processing) for classification problems. However, modeling data as a graph in medical domain is not always obvious. Recently, many researches are interested on defining a data structure with more than information channel to take advantage of their mix , so they are constructing graphs to apply deep learning on.