Python库 | clinicadl-0.2.0.tar.gz

<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](https://aramislab.paris.inria.fr/clinica/docs/public/latest/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](https://aramislab.paris.inria.fr/clinica/docs/public/latest/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)