The study of neurodegenerative diseases relies on the reconstruction and analysis of the
brain cortex from magnetic resonance imaging (MRI). Traditional frameworks for this task like …

This paper aims at determining under which conditions the semi-discrete optimal transport
is twice differentiable with respect to the parameters of the discrete measure and exhibits …

Recent advances in geometric deep-learning introduce complex computational challenges
for evaluating the distance between meshes. From a mesh model, point clouds are …

We propose a fast and scalable algorithm to project a given density on a set of structured
measures defined over a compact 2D domain. The measures can be discrete or supported on …

In this paper, we introduce CorticalFlow, a new geometric deep-learning model that, given a
3-dimensional image, learns to deform a reference template towards a targeted object. To …

The problem of Cortical Surface Reconstruction from magnetic resonance imaging has
been traditionally addressed using lengthy pipelines of image processing techniques like …

Cortical thickness (CTh) is routinely used to quantify grey matter atrophy as it is a significant
biomarker in studying neurodegenerative and neurological conditions. Clinical studies …

In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a
3-dimensional image, learns to deform a reference template towards a targeted object. To …

Brain morphometry from magnetic resonance imaging (MRI) is commonly used for estimating
imaging biomarkers for many neurodegenerative diseases, including Alzheimer's. Recent …

Deep learning models are powerful tools for addressing challenging medical imaging
problems. However, for an ever-growing range of applications, interpreting a model's prediction …