416 citations · 451 across the 6 of their papers we have counts for
8 papers
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…
Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data
Pascal Lu, Olivier Colliot
In this paper, we propose a framework for automatic classification of patients from multimodal genetic and brain imaging data by optimally combining them. Additive models with unad…
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…
Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI
Kuldeep Kumar, Laurent Chauvin, Mathew Toews +2
So far, fingerprinting studies have focused on identifying features from single-modality MRI data, which capture individual characteristics in terms of brain structure, function, o…
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…