Statistical shape modeling (SSM) is widely used in biology and medicine ...
Conditional correlation networks, within Gaussian Graphical Models (GGM)...
The Expectation Maximisation (EM) algorithm is widely used to optimise
n...
Gaussian Graphical Models (GGM) are often used to describe the condition...
We present the findings of "The Alzheimer's Disease Prediction Of
Longit...
In the past two years, over 30 papers have proposed to use convolutional...
The ability to predict the progression of biomarkers, notably in NDD, is...
Diffusion MRI is the modality of choice to study alterations of white ma...
A large number of papers have introduced novel machine learning and feat...
Multiple sclerosis (MS) is a demyelinating disease of the central nervou...
We propose a method to learn a distribution of shape trajectories from
l...
The analysis of manifold-valued data requires efficient tools from Riema...
We propose a method to predict the subject-specific longitudinal progres...
We introduce a mixed-effects model to learn spatiotempo-ral patterns on ...
In recent years, the number of papers on Alzheimer's disease classificat...
The extraction of fibers from dMRI data typically produces a large numbe...