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09/07/2020
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Statistical shape modeling (SSM) is widely used in biology and medicine ...
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06/19/2020
Mixture of Conditional Gaussian Graphical Models for unlabelled heterogeneous populations in the presence of co-factors
Conditional correlation networks, within Gaussian Graphical Models (GGM)...
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03/23/2020
Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM
The Expectation Maximisation (EM) algorithm is widely used to optimise n...
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03/11/2020
Gaussian Graphical Model exploration and selection in high dimension low sample size setting
Gaussian Graphical Models (GGM) are often used to describe the condition...
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02/09/2020
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
We present the findings of "The Alzheimer's Disease Prediction Of Longit...
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04/16/2019
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
In the past two years, over 30 papers have proposed to use convolutional...
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04/05/2019
Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects
The ability to predict the progression of biomarkers, notably in NDD, is...
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12/28/2018
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
Diffusion MRI is the modality of choice to study alterations of white ma...
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08/20/2018
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data
A large number of papers have introduced novel machine learning and feat...
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04/21/2018
Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training
Multiple sclerosis (MS) is a demyelinating disease of the central nervou...
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03/27/2018
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
We propose a method to learn a distribution of shape trajectories from l...
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11/23/2017
Parallel transport in shape analysis: a scalable numerical scheme
The analysis of manifold-valued data requires efficient tools from Riema...
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11/23/2017
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
We propose a method to predict the subject-specific longitudinal progres...
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09/25/2017
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
We introduce a mixed-effects model to learn spatiotempo-ral patterns on ...
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09/21/2017
Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease
In recent years, the number of papers on Alzheimer's disease classificat...
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09/18/2017
