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Conference Papers Year : 2019

Riemannian Geometry Learning for Disease Progression Modelling


The analysis of longitudinal trajectories is a longstandingproblem in medical imaging which is often tackled in the context ofRiemannian geometry: the set of observations is assumed to lie on an apriori known Riemannian manifold. When dealing with high-dimensionalor complex data, it is in general not possible to design a Riemanniangeometry of relevance. In this paper, we perform Riemannian manifoldlearning in association with the statistical task of longitudinal trajectoryanalysis. After inference, we obtain both a submanifold of observationsand a Riemannian metric so that the observed progressions are geodesics.This is achieved using a deep generative network, which maps trajectoriesin a low-dimensional Euclidean space to the observation space.

Dates and versions

hal-02429839 , version 1 (06-01-2020)



Maxime Louis, Raphäel Couronné, Igor Koval, Benjamin Charlier, Stanley Durrleman. Riemannian Geometry Learning for Disease Progression Modelling. Information Processing in Medical Imaging - IPMI 2019, Jun 2019, Hong-Kong, China. pp.542-553, ⟨10.1007/978-3-030-20351-1_42⟩. ⟨hal-02429839⟩
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