Metadata-Version: 2.1
Name: clinicadl
Version: 0.1.0rc2
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)**. This work
        has been published in [Medical Image
        Analysis](https://doi.org/10.1016/j.media.2020.101694) and is also available on
        [arXiv](https://arxiv.org/abs/1904.07773).
        
        Automatic classification of AD using classical machine learning approaches can
        be performed using the framework available here:
        <https://github.com/aramis-lab/AD-ML>.
        
        > **Disclaimer:** this software is **under development**. Some features can
        change between different commits. A stable version is planned to be released
        soon. The release v.0.0.1 corresponds to the date of submission of the
        publication but in the meantime important changes are being done to facilitate
        the use of the package.
        
        The complete documentation of the project can be found on 
        this [page](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!
        
        ## Overview
        
        ### How to use ClinicaDL?
        
        `clinicadl` is an utility that is used through the command line. Several tasks
        can be performed:
        
        - **Preparation of your imaging data**
            * **T1w-weighted MR image preprocessing.** The `preprocessing` task
              processes a dataset of T1 images stored in BIDS format and prepares to
              extract the tensors (see paper for details on the preprocessing). Output
              is stored using the [CAPS](http://www.clinica.run/doc/CAPS/Introduction/)
              hierarchy.
            * **Quality check of preprocessed data.** The `quality_check` task uses a
              pretrained network [(Fonov et al,
              2018)](https://www.biorxiv.org/content/10.1101/303487v1) to classify
              adequately registered images.
            * **Tensor extraction from preprocessed data.** The `extract` task allows
              to create files in PyTorch format (`.pt`) with different options: the
              complete MRI, 2D slices and/or 3D patches. This files are also stored in
              the [CAPS](http://www.clinica.run/doc/CAPS/Introduction/) hierarchy.
        
        - **Train & test your classifier**
            * **Train neural networks.** The `train` task is designed to perform
              training of CNN models using different kind of inputs, e.g., a full MRI
              (3D-image), patches from a MRI (3D-patch), specific regions of a MRI
              (ROI-based) or slices extracted from the MRI (2D-slices). Parameters used
              during the training are configurable. This task allow also to train
              autoencoders.
            * **MRI classification.** The `classify` task uses previously trained models
              to perform the inference of a particular or a set of MRI.
        
        
        - **Utilitaries used for the preparation of imaging data and/or training your
          classifier**
            * **Process TSV files**. `tsvtool` includes many functions to get labels
              from BIDS, perform k-fold or single splits, produce demographic analysis
              of extracted labels and reproduce the restrictions made on AIBL and OASIS
              in the original paper.
            * **Generate a synthetic dataset.** The `generate` task is useful to obtain
              synthetic datasets frequently used in functional tests.
        
        ## Pretrained models
        
        Some of the pretained models for the CNN networks described in 
        ([Wen et al., 2020](https://doi.org/10.1016/j.media.2020.101694)) 
        are available on Zenodo:
        <https://zenodo.org/record/3491003>
        
        Updated versions of the models will be published soon.
        
        ## 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)
        
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
