https://github.com/aramis-lab/AD-ML

Framework for the reproducible classification of Alzheimer's disease using machine learning
https://github.com/aramis-lab/AD-ML

alzheimer-disease machine-learning neuroimaging python scikit-learn

Last synced: 3 months ago
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Framework for the reproducible classification of Alzheimer's disease using machine learning

• Host: GitHub
• URL: https://github.com/aramis-lab/AD-ML
• Owner: aramis-lab
• License: mit
• Created: 2019-06-06T16:45:50.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-05-31T09:57:09.000Z (over 2 years ago)
• Last Synced: 2024-07-14T18:44:37.600Z (4 months ago)
• Topics: alzheimer-disease, machine-learning, neuroimaging, python, scikit-learn
• Language: Python
• Homepage:
• Size: 3.3 MB
• Stars: 38
• Watchers: 7
• Forks: 15
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
This repository contains a software framework for **reproducible machine learning experiments on automatic classification of Alzheimer's disease (AD)** using multimodal MRI and PET data from three publicly available datasets [ADNI](http://adni.loni.usc.edu/), [AIBL](https://aibl.csiro.au/research/neuroimaging/), [OASIS](http://www.oasis-brains.org/). It is developed by the [ARAMIS Lab](http://www.aramislab.fr).

In the directory [Generic Version](Generic_Version), there are examples of data conversion, preprocessing and classification tasks, that show how to use the different features of [Clinica software](http://www.clinica.run). This code relies on the latest released version of Clinica.

If you are interested in accessing the repositories containing the code of the experiments and results of our papers that use Clinica, please change to the branch of the corresponding paper:

* [2019 - Reproducible evaluation of methods for predicting progression to Alzheimer’s disease from clinical and neuroimaging data](https://github.com/aramis-lab/AD-ML/tree/2019_SPIE)

* [2019 - Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer’s disease](https://github.com/aramis-lab/AD-ML/tree/2019_DTI)

* [2018 - Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data](https://github.com/aramis-lab/AD-ML/tree/2018_NeuroImage)

* [2017 - Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease](https://github.com/aramis-lab/AD-ML/tree/2017_MLMI)

# Citing this work

If you use this software, please cite:

> J. Samper-Gonzalez, N. Burgos, S. Bottani, S. Fontanella, P. Lu, A. Marcoux, A. Routier, J. Guillon, M. Bacci, J. Wen, A. Bertrand, H. Bertin, M.-O. Habert, S. Durrleman, T. Evgeniou and O. Colliot, **Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data**. NeuroImage, 183:504–521, 2018 [doi:10.1016/j.neuroimage.2018.08.042](https://doi.org/10.1016/j.neuroimage.2018.08.042) - [Paper in PDF](https://hal.inria.fr/hal-01858384/document) - [Supplementary material](https://hal.inria.fr/hal-01858384/file/supplementary_data.xlsx)

>

In addition, if you use Diffusion MRI data or related code, please cite:

> J. Wen, J. Samper-Gonzalez, S. Bottani, A. Routier, N. Burgos,  T. Jacquemont, S. Fontanella,  S. Durrleman, S. Epelbaum, A. Bertrand,  and O. Colliot, **Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer’s disease**. Submitted for publication

>

# Documentation

This code relies heavily on the [Clinica software platform](http://www.clinica.run) that you will need to install.

The documentation is available at: https://github.com/aramis-lab/AD-ML/wiki