Tidy workflows for Neuroimaging
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Updated
Nov 10, 2020 - R
Tidy workflows for Neuroimaging
MWPCR-with-Matlab
Brain networks processing and data analysis. This project uses a graph-theory approach to analyse PET 18F-FDG (a glucose metabolism radioactive tracer) neuroimage data in human or rodent subjects.
Implementation MWPCR with R
Source code from PhD thesis.
This is an experimental Docker image containing MIRTK, DrawEM, and FSL.
Predicting Suicidality with ABCD(Adolescent Brain Cognitive Development) dataset using TabNet
Inter-Regional High-level Relation Learning from Functional Connectivity via Self-Supervision - PyTorch Implementation (MICCAI 2021)
Integrating machining learning and multi-modal neuroimaging to detect schizophrenia at the level of the individual
[KHBM] Winter School 2022 - Neuroimage Data Analysis using Python and Graph Neural Networks
Deep Recurrent Model for Individualized Prediction of Alzheimer’s Disease Progression - PyTorch Implementation (NeuroImage 2021)
Group-wise diffeomorphic registration and segmentation of medical images.
Brain CT image segmentation, normalisation, skull-stripping and total brain/intracranial volume computation.
This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"
Comprehensive and open-source library of analysis tools for MRI of the spinal cord.
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