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awesome-vaes

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

benchmark_cnn_fmri

Benchmarking of fmri decoding using cnn models

bpsc

Brain parcellation using structural covariance

broca_parcels

Parcellation of Broca's region using resting-state fMRI

cbig

cneuromod_docs

Documentation of the Courtois NeuroMod project

dgl

The official repo is https://github.com/dmlc/dgl . THIS IS A FORK.

functional-vs-geometric-parcellation

Comparison of Functional vs Geometric parcellation of human brain

gcn_fmri_decoding

Brain decoding using graph convolutional network on fMRI time-series

gcn_tutorial

this tutorial covers the basics of graph laplacian and graph convolutional networks and how to apply these tools to neuroimging data

gcn_tutorial_test

gnnpapers

Must-read papers on graph neural networks (GNN)

ivado-mila-dl-school-2019-vancouver

nilearn

Machine learning for NeuroImaging in Python

p-and-a

Automatically exported from code.google.com/p/p-and-a

pytorch-vae

A Collection of Variational Autoencoders (VAE) in PyTorch.

subdivision-of-human-substantia-nigra

We investigated the anatomical and functional organization of the human substantia nigra (SN) using diffusion and functional MRI data from the Human Connectome Project. A tripartite connectivity-based parcellation of SN was identified with a limbic, cognitive, motor arrangement.

unet-tensorflow-keras

A concise code for training and evaluating Unet using tensorflow+keras

zhangyu2ustc.github.io

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