output
20182022
most citedLearning a Probabilistic Model for Diffeomorphic Registration

250 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2018Show all

8 papers · 1 filter

cs.CV2018★ 250 cited

Learning a Probabilistic Model for Diffeomorphic Registration

Julian Krebs, Hervé Delingette, Boris Mailhé +2

We propose to learn a low-dimensional probabilistic deformation model from data which can be used for registration and the analysis of deformations. The latent variable model maps…

math.DG2018

Math in the Black Forest: Workshop on New Directions in Shape Analysis

Martin Bauer, Nicolas Charon, Philipp Harms +9

These are the proceedings of the workshop "Math in the Black Forest", which brought together researchers in shape analysis to discuss promising new directions. Shape analysis is an…

stat.ME2018

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

Luigi Antelmi, Nicholas Ayache, Philippe Robert +1

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly…

math.DG2018★ 7 cited

Parallel Transport with Pole Ladder: a Third Order Scheme in Affine Connection Spaces which is Exact in Affine Symmetric Spaces

Xavier Pennec

Parallel transport is an important step in many discrete algorithms for statistical computing on manifolds. Numerical methods based on Jacobi fields or geodesics parallelograms are…

cs.CV2018★ 4 cited

3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation

Qiao Zheng, Hervé Delingette, Nicolas Duchateau +1

We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (arou…

cs.CV2018★ 47 cited

Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training

Wen Wei, Emilie Poirion, Benedetta Bodini +4

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS). A reliable measure of the tissue myelin content is therefore essential for the understanding…

cs.CV2018★ 2 cited

3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation

Qiao Zheng, Hervé Delingette, Nicolas Duchateau +1

We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it…

stat.ML2018★ 14 cited

Constraining the Dynamics of Deep Probabilistic Models

Marco Lorenzi, Maurizio Filippone

We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics. In particular, we develop a flexible methodolog…