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ClinicaDL

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

Build Status PyPI version Documentation Status

Documentation | Tutorial | Forum | See also: AD-ML, Clinica

About the project

This repository hosts the source code of a framework for the reproducible evaluation of deep learning classification experiments using anatomical MRI data for the computer-aided diagnosis of Alzheimer's disease (AD).

Disclaimer: this software is under development. Some features can change between different releases and/or commits.

To access the full documentation of the project, follow the link https://clinicadl.readthedocs.io/. If you find a problem when using it or if you want to provide us feedback, please open an issue or write on the forum.

Getting started

ClinicaDL currently supports macOS and Linux.

We recommend to use conda or virtualenv for the installation of ClinicaDL as it guarantees the correct management of libraries depending on common packages:

conda create --name ClinicaDL python=3.7
conda activate ClinicaDL
pip install clinicadl

⚠️ NEW!: ⚠️

🎗️ Visit our hands-on tutorial web site to start using ClinicaDL directly in a Google Colab instance!

Related Repositories

Citing us

  • Wen, J., Thibeau-Sutre, E., Samper-González, J., Routier, A., Bottani, S., Durrleman, S., Burgos, N., and Colliot, O.: ‘Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation’, Medical Image Analysis, 63: 101694, 2020. doi:10.1016/j.media.2020.101694
  • Routier, A., Burgos, N., Díaz, M., Bacci, M., Bottani, S., El-Rifai O., Fontanella, S., Gori, P., Guillon, J., Guyot, A., Hassanaly, R., Jacquemont, T., Lu, P., Marcoux, A., Moreau, T., Samper-González, J., Teichmann, M., Thibeau-Sutre, E., Vaillant G., Wen, J., Wild, A., Habert, M.-O., Durrleman, S., and Colliot, O.: ‘Clinica: An Open Source Software Platform for Reproducible Clinical Neuroscience Studies’, 2021. hal-02308126

Reproducibility

To reproduce the results published in Wen et al., MedIA, 2020 (arXiv version) please use the version of ClinicaDL tagged [v0.0.1](https://github.com/aramis-lab/AD-DL/tree/v.0.0.1).

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

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