821 citations · 1.2k across the 15 of their papers we have counts for
18 papers
Clinica: an open source software platform for reproducible clinical neuroscience studies
Alexandre Routier, Ninon Burgos, Mauricio Díaz +21
We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to…
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Anupama Goparaju, Alexandre Bone, Nan Hu +8
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technolog…
Mixture of Conditional Gaussian Graphical Models for unlabelled heterogeneous populations in the presence of co-factors
Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière
Conditional correlation networks, within Gaussian Graphical Models (GGM), are widely used to describe the direct interactions between the components of a random vector. In the case…
Gaussian Graphical Model exploration and selection in high dimension low sample size setting
Thomas Lartigue, Simona Bottani, Stephanie Baron +3
Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM i…
Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM
Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière
The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more sp…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…
Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects
Igor Koval, Stéphanie Allassonnière, Stanley Durrleman
The ability to predict the progression of biomarkers, notably in NDD, is limited by the size of the longitudinal data sets, in terms of number of patients, number of visits per pat…
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo +7
Over 30 papers have proposed to use convolutional neural network (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare ac…
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
Junhao Wen, Jorge Samper-Gonzalez, Simona Bottani +8
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of AD. However, clas…
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
Jorge Samper-González, Ninon Burgos, Simona Bottani +13
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of AD. However, they are difficult to reproduce because…
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…
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
Alexandre Bône, Olivier Colliot, Stanley Durrleman
We propose a method to learn a distribution of shape trajectories from longitudinal data, i.e. the collection of individual objects repeatedly observed at multiple time-points. The…
Parallel transport in shape analysis: a scalable numerical scheme
Maxime Louis, Alexandre Bône, Benjamin Charlier +1
The analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake. This complexity arises from the always-in…
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
Alexandre Bône, Maxime Louis, Alexandre Routier +5
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease da…
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier +4
We introduce a mixed-effects model to learn spatiotempo-ral patterns on a network by considering longitudinal measures distributed on a fixed graph. The data come from repeated obs…
Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease
Jorge Samper-González, Ninon Burgos, Sabrina Fontanella +5
In recent years, the number of papers on Alzheimer's disease classification has increased dramatically, generating interesting methodological ideas on the use machine learning and…
White Matter Fiber Segmentation Using Functional Varifolds
Kuldeep Kumar, Pietro Gori, Benjamin Charlier +3
The extraction of fibers from dMRI data typically produces a large number of fibers, it is common to group fibers into bundles. To this end, many specialized distance measures, suc…
Linear Thompson Sampling Revisited
Marc Abeille, Alessandro Lazaric
We derive an alternative proof for the regret of Thompson sampling (\ts) in the stochastic linear bandit setting. While we obtain a regret bound of order $\widetilde{O}(d^{3/2}\sqr…