Metadata-Version: 2.1
Name: clinicadl
Version: 0.2.2
Summary: Deep learning classification with clinica
Home-page: https://github.com/aramis-lab/AD-DL
Author: ARAMIS Lab
Maintainer: Mauricio DIAZ
Maintainer-email: mauricio.diaz@inria.fr
License: MIT license
Description: <h1 align="center">
          <a href="http://www.clinica.run">
            <img src="http://www.clinica.run/assets/images/clinica-icon-257x257.png" alt="Clinica Logo" width="120" height="120">
          </a>
          +
          <a href="https://pytorch.org/">
            <img src="https://pytorch.org/assets/images/pytorch-logo.png" alt="PyTorch Logo" width="120" height="120">
          </a>
          <br/>
          ClinicaDL
        </h1>
        
        <p align="center"><strong>Framework for the reproducible classification of Alzheimer's disease using deep learning</strong></p>
        
        <p align="center">
          <a href="https://ci.inria.fr/clinicadl/job/AD-DL/job/master/">
            <img src="https://ci.inria.fr/clinicadl/buildStatus/icon?job=AD-DL%2Fmaster" alt="Build Status">
          </a>
          <a href="https://badge.fury.io/py/clinicadl">
            <img src="https://badge.fury.io/py/clinicadl.svg" alt="PyPI version">
          </a>
          <a href='https://clinicadl.readthedocs.io/en/latest/?badge=latest'>
            <img src='https://readthedocs.org/projects/clinicadl/badge/?version=latest' alt='Documentation Status' />
          </a>
        
        </p>
        
        <p align="center">
          <a href="https://clinicadl.readthedocs.io/">Documentation</a> |
          <a href="https://aramislab.paris.inria.fr/clinicadl/tuto/intro.html">Tutorial</a> |
          <a href="https://groups.google.com/forum/#!forum/clinica-user">Forum</a> |
          See also:
          <a href="#related-repositories">AD-ML</a>,
          <a href="#related-repositories">Clinica</a>
        </p>
        
        
        ## 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/](https://clinicadl.readthedocs.io/). 
        If you find a problem when using it or if you want to provide us feedback,
        please [open an issue](https://github.com/aramis-lab/ad-dl/issues) or write on
        the [forum](https://groups.google.com/forum/#!forum/clinica-user).
        
        ## 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:
        
        ```{.sourceCode .bash}
        conda create --name ClinicaDL python=3.7
        conda activate ClinicaDL
        pip install clinicadl
        ```
        
        :warning: **NEW!:** :warning:
        > :reminder_ribbon: Visit our [hands-on tutorial web
        site](https://aramislab.paris.inria.fr/clinicadl/tuto/intro.html) to start
        using **ClinicaDL** directly in a Google Colab instance!
        
        ## Related Repositories
        
        - [Clinica: Software platform for clinical neuroimaging studies](https://github.com/aramis-lab/clinica)
        - [AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning](https://github.com/aramis-lab/AD-ML)
        
        ## 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](https://doi.org/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](https://hal.inria.fr/hal-02308126)
        
        
        ## Reproducibility
        
        To reproduce the results published in [Wen et al., MedIA, 2020](https://doi.org/10.1016/j.media.2020.101694) ([arXiv version](https://arxiv.org/abs/1904.07773))
        please use the version of ClinicaDL tagged `[v0.0.1](https://github.com/aramis-lab/AD-DL/tree/v.0.0.1)`.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Requires-Python: >=3.6
Description-Content-Type: text/markdown
