9 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- CEA Paris-SaclayFR8 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR8 papers
- Télécom ParisFR3 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- Direction de la Recherche FondamentaleFR2 papers
- Université Paris-SaclayFR2 papers
- Centre Hospitalier Sainte-AnneFR1 paper
- Cognitive Neuroimaging LabFR1 paper
- École PolytechniqueFR1 paper
- GHU Paris psychiatrie & neurosciences1 paper
- Harvard UniversityUS1 paper
- Hôpital Raymond-PoincaréFR1 paper
9 papers
Self-supervised contrastive learning unveils cortical folding pattern linked to prematurity
Julien Laval, Aymeric Gaudin, Vincent Frouin +5
Brain folding patterns have been reported to carry clinically relevant information. The brain folds mainly during the last trimester of pregnancy, and the process might be durably…
SNAKE-fMRI: A modular fMRI data simulator from the space-time domain to k-space and back
Pierre-Antoine Comby, Alexandre Vignaud, Philippe Ciuciu
We propose a new, modular, open-source, Python-based 3D+time fMRI data simulation software, \emph{SNAKE-fMRI}, which stands for \emph{S}imulator from \emph{N}eurovascular coupling…
Separating common from salient patterns with Contrastive Representation Learning
Robin Louiset, Edouard Duchesnay, Antoine Grigis +1
Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a…
SepVAE: a contrastive VAE to separate pathological patterns from healthy ones
Robin Louiset, Edouard Duchesnay, Antoine Grigis +2
Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that aims at separating the common factors of variation between a background dataset (BG) (i.e.,…
GeoLab: Geometry-based Tractography Parcellation of Superficial White Matter
Nabil Vindas, Nicole Labra Avila, Fan Zhang +3
Superficial white matter (SWM) has been less studied than long-range connections despite being of interest to clinical research, andfew tractography parcellation methods have been…
Identification of Rare Cortical Folding Patterns using Unsupervised Deep Learning
Louise Guillon, Joël Chavas, Audrey Bénézit +3
Like fingerprints, cortical folding patterns are unique to each brain even though they follow a general species-specific organization. Some folding patterns have been linked with n…
Unbiased Supervised Contrastive Learning
Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione +2
Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distributio…
Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GAN
André Ferreira, Ricardo Magalhães, Sébastien Mériaux +1
Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-hig…
Optimizing full 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance Imaging
Chaithya G R, Pierre Weiss, Guillaume Daval-Frérot +3
The Spreading Projection Algorithm for Rapid K-space samplING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D T2*-w MRI using c…