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SL-DRT-24-0589

Published on 7 December 2023
SL-DRT-24-0589
Research fieldHealth and environment technologies, medical devices

Domaine-SMathematics - Numerical analysis - Simulation

ThemeTechnological challenges

Theme-SEngineering sciences

Field
Health and environment technologies, medical devices Technological challenges Mathematics - Numerical analysis - Simulation Engineering sciences DRT DTBS SYMS LMTS Grenoble
Title
Geometric deep learning applied to medical applications
Abstract
The PhD subject deals with geometric deep learning and its use in several medical applications. The merging of these two domains (geometry and artificial intelligence) is at the core of the phD with the conception of SPDnet neural networks that combine both end-to-end training of frequency and spatial parameters with mathematical operations on the variety of symmetric definite-positive (SPD) matrices. The design of such methods both from a mathematical and software point of view are part of the phD’s objectives as well as their application on public medical datasets like in electroencephalography-based brain-computer interface (BCI). The expected results consist first in demonstrating the superiority of these geometric approaches over state-of-the-art methods used in BCI and second to identify the best architectures in different medical applications ranging from multi-array data to medical image processing.
Formation
mathématiques appliqués, IA Technological Research
Contact person
BONNET Stéphane CEA DRT/DTBS//LMTS 17, rue des Martyrs 38054 Grenoble Cedex 9 France 04 38 78 40 70 stephane.bonnet@cea.fr
University/ graduate school
Université Grenoble Alpes Ingénierie pour la Santé, la Cognition et l’Environnement (EDISCE)
Thesis supervisor
 
Location
Département Microtechnologies pour la Biologie et la Santé (LETI) Service des sYstèmes de Mesure pour la Santé Laboratoire Mesure et Traitement des Signaux Physiologiques
Start1/9/2024

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