Framework for the reproducible processing of neuroimaging data with deep learning methods
Project description
ClinicaDL
Framework for the reproducible processing of neuroimaging data with deep learning methods
Documentation | Tutorial | Forum
About the project
This repository hosts ClinicaDL, the deep learning extension of Clinica, a python library to process neuroimaging data in BIDS format.
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=3.8
conda activate ClinicaDL
pip install clinicadl
Tutorial
Visit our hands-on tutorial web site to start using ClinicaDL directly in a Google Colab instance!
Related Repositories
- Clinica: Software platform for clinical neuroimaging studies
- AD-DL: Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
- AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning
Citing us
- Thibeau-Sutre, E., Díaz, M., Hassanaly, R., Routier, A., Dormont, D., Colliot, O., Burgos, N.: ‘ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing‘, 2021. hal-03351976
- 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. doi:10.3389/fninf.2021.689675 Open Access version
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file clinicadl-1.6.1.tar.gz.
File metadata
- Download URL: clinicadl-1.6.1.tar.gz
- Upload date:
- Size: 170.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 3aede4400129053e16a4379fed9cf0b040c0610a6cec876bc07ea7436d8f9841 |
|
| MD5 | c74eee9ff9ab399f12b654a95da1b635 |
|
| BLAKE2b-256 | 33dc31a01c2f0c47e748c822da45155db2b05be1d3b4f09a87df980553398ffd |
File details
Details for the file clinicadl-1.6.1-py3-none-any.whl.
File metadata
- Download URL: clinicadl-1.6.1-py3-none-any.whl
- Upload date:
- Size: 237.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | abe38c1a578e84394d6dc01a64f1da7a57f0e81273123ffc0355eee7d4c842da |
|
| MD5 | dd612798e86a24ccd962bd2d2282d4c3 |
|
| BLAKE2b-256 | 19814e62b717480907921db269341933ce9549f19a6f64a11d033172713280e2 |
