neurospin-projects Goto Github PK
Name: NeuroSpin - Projects
Type: Organization
Bio: Neuroimaging research centre
Location: France
Blog: https://joliot.cea.fr/drf/joliot/Pages/Entites_de_recherche/NeuroSpin.aspx
Name: NeuroSpin - Projects
Type: Organization
Bio: Neuroimaging research centre
Location: France
Blog: https://joliot.cea.fr/drf/joliot/Pages/Entites_de_recherche/NeuroSpin.aspx
Learn human sulcus variability using GAN
Hippocampal Segmentation Factory (HSF). A one-liner Deep Learning tool to segment raw T2w (and T1w) MRIs into hippocampal subfields in few seconds. Trained on 700+ manually segmented hippocampi.
Characterize cortical folding patterns around Superior Temporal Sulcus thanks to contrastive learning tools
Compare beta-VAE and SimCLR to detect sulcus patterns in the cingulate region
UCSL: Unsupervised Clustering driven by Supervised Learning
Try several methods for MRI reconstruction on the fastmri dataset. Home to the XPDNet, runner-up of the 2020 fastMRI challenge.
A robust, easy-to-deploy non-uniform Fast Fourier Transform in TensorFlow.
Reproducing results described in "Deep Learning Improvement over Standard Machine Learning in Anatomical Neuroimaging comes from Transfer Learning"
Mixture-of-Products-of-Experts (MoPoE) VAE
Multi-modal benchmark
MICCAI 2022: data augmentation on the surface - application to contrastive learning.
Use contrastive learning to predict cognitive parameters from skeletons
Identification of rare cortical folding patterns based on an unsupervised deep learning approach
UNet for automatic labelling of chimpanzee cortical sulci
Supervised learning on folding skeleton crops
Consistent time embeddings of high-dimensional fMRI recordings using auxiliary variables.
Toy ML example: predict age on localizer data
STS babies project
Contrastive learning on sulci skeleton
Supervised Diagnosis Prediction from Cortical Sulci: Toward the Discovery of Neurodevelopmental Biomarkers in Mental Disorders
GitHub repository of the code and examples of Deep UCSL.
SepVAE
SepCLR is a Contrastive Learning methods made to address Contrastive Analysis paradigm.
Predict age from brain anatomy measures of Grey Matter (GM) volumes using Deep Learning.
Age prediction with site-effect removal: A challenge on the openBHB dataset that aims to i) predict age from derived 3D T1w anatomical MRI data while ii) removing site/scanner information from the learned representation.
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