Riemannian geometry learning for disease progression modelling - INRIA - Institut National de Recherche en Informatique et en Automatique Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2019

Riemannian geometry learning for disease progression modelling

Résumé

The analysis of longitudinal trajectories is a longstanding problem in medical imaging which is often tackled in the context of Riemannian geometry: the set of observations is assumed to lie on an a priori known Riemannian manifold. When dealing with high-dimensional or complex data, it is in general not possible to design a Riemannian geometry of relevance. In this paper, we perform Riemannian manifold learning in association with the statistical task of longitudinal trajectory analysis. After inference, we obtain both a submanifold of observations and a Riemannian metric so that the observed progressions are geodesics. This is achieved using a deep generative network, which maps trajectories in a low-dimensional Euclidean space to the observation space.
Fichier principal
Vignette du fichier
IPMI2019_camera_ready(1).pdf (1.07 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02079820 , version 1 (26-03-2019)
hal-02079820 , version 2 (17-04-2019)

Identifiants

  • HAL Id : hal-02079820 , version 1

Citer

Maxime Louis, Raphäel Couronné, Igor Koval, Benjamin Charlier, Stanley Durrleman. Riemannian geometry learning for disease progression modelling. 2019. ⟨hal-02079820v1⟩

Collections

INSERM
495 Consultations
1110 Téléchargements

Partager

Gmail Facebook X LinkedIn More