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30 search results found

Mne Python ⭐ 2,449

MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

Deep Learning Toolkit for Medical Image Analysis

Nilearn ⭐ 1,076

Machine learning for NeuroImaging in Python

DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

Brainiak ⭐ 315

Brain Imaging Analysis Kit

Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain

Clinica ⭐ 201

Software platform for clinical neuroimaging studies

Nobrainer ⭐ 139

A framework for developing neural network models for 3D image processing.

Canlabcore ⭐ 135

Core tools required for running Canlab Matlab toolboxes. The heart of this toolbox is object-oriented tools that enable interactive analysis of neuroimaging data and simple scripts using high-level commands tailored to neuroimaging analysis.

Neuropredict ⭐ 99

Easy and comprehensive assessment of predictive power, with support for neuroimaging features

Micaopen ⭐ 58

Open Scripts and pipelines from the Multimodal Imaging and Connectome Analysis Lab at the Montreal Neurological Institute

Confounds ⭐ 35

Conquering confounds and covariates in machine learning

Actflowtoolbox ⭐ 29

The Brain Activity Flow ("Actflow") Toolbox. Tools to quantify the relationship between connectivity and task activity through network simulations and machine learning prediction. Helps determine how connections contribute to specific brain functions.

Subject-wise networks from structural MRI, both vertex- and voxel-wise features (thickness, GM density, curvature, gyrification)

Awesome Medical Imaging ⭐ 26

Awesome list of software that I use to do research in medical imaging.

Framework for the reproducible classification of Alzheimer's disease using machine learning

BRAPH 2.0 is a comprehensive software package for the analysis and visualization of brain connectivity data, offering flexible customization, rich visualization capabilities, and a platform for collaboration in neuroscience research.

Pyradigm ⭐ 24

Research data management in biomedical and machine learning applications

An easy to use decoding toolbox for Matlab users.

Easylearn_dev ⭐ 20

Easylearn is designed for machine learning mainly in resting-state fMRI, radiomics and other fields (such as EEG). Easylearn is built on top of scikit-learn, pytorch and other packages. Easylearn can assist doctors and researchers who have limited coding experience to easily realize machine learning, e.g., (MR/CT/PET/EEG)imaging-marker- or other biomarker-based disease diagnosis and prediction, treatment response prediction, disease subtyping, dimensional decoding for transdiagnostic psychiatri

Brainmage ⭐ 20

Brain extraction in presence of abnormalities, using single and multiple MRI modalities

Neuroml2020 ⭐ 20

Machine Learning in Neuroimaging data, Fall 2020.

Nbs Predict ⭐ 19

A prediction-based extension of network-based statistics.

Text2brain ⭐ 15

Generating brain activation maps from free-form text query

Utilities and tools for machine learning on BOLD-fMRI data

Neural_pre_processing ⭐ 11

Neural Pre Processing is an end-to-end weakly supervised learning approach for converting raw head MRI images to intensity-normalized, skull-stripped brain in a standard coordinate space

Friendengine ⭐ 10

FRIEND engine is a new implementation of the FRIEND software, a fMRI neurofeedback processing package, already available on the NITRC repository

The Brain Predictability toolbox (BPt), is a python based Machine Learning library designed primarily for tabular and neuroimaging specific neuroimaging data but can easily be generalized further.

Histogram-weighted Networks for Connectivity & Advanced Analysis in Neuroscience

Deepretinotopy ⭐ 5

Code for predicting retinotopy from anatomy using geometric deep learning. See paper for more --> https://www.sciencedirect.com/science/article/pii/

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