Train AI models efficiently on medical images using any framework
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Updated
May 26, 2024 - Python
Train AI models efficiently on medical images using any framework
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Benchmarks for functional connectivity estimators and FCEst Python package
Brain Imaging Data Structure (BIDS) Specification
Methods for estimating time-varying functional connectivity (TVFC)
Machine learning for NeuroImaging in Python
MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python.
Framework for the reproducible processing of neuroimaging data with deep learning methods
Workflows and interfaces for neuroimaging packages
Example Python, Matlab, and Jupyter notebook code using HED (Hierarchical Event Descriptors). Includes BIDS-compatible test datasets.
Coordinate- and image-based meta-analysis in Python
Forschungszentrum Jülich Neuroimaging Feature Extractor
ENIGMA HALFpipe is a user-friendly software that facilitates reproducible analysis of fMRI data
A toolbox for comparing brain maps
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.
BIDScoin converts your source-level neuroimaging data to BIDS
TE-dependent analysis of multi-echo fMRI
Software platform for clinical neuroimaging studies
Python package to access a cacophony of neuro-imaging file formats
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