Framework for the reproducible processing of neuroimaging data with deep learning methods
Framework for the reproducible processing of neuroimaging data with deep learning methods
Documentation | Tutorial | Forum
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.
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
Visit our hands-on tutorial web site to start using ClinicaDL directly in a Google Colab instance!
FAQs
Framework for the reproducible processing of neuroimaging data with deep learning methods
We found that clinicadl demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
OpenSSF has published OSPS Baseline, an initiative designed to establish a minimum set of security-related best practices for open source software projects.
Security News
Michigan TypeScript founder Dimitri Mitropoulos implements WebAssembly runtime in TypeScript types, enabling Doom to run after processing 177 terabytes of type definitions.
Security News
vlt's new "reproduce" tool verifies npm packages against their source code, outperforming traditional provenance adoption in the JavaScript ecosystem.