View monai-weekly on Snyk Open Source Advisor

Package Health Score

86 / 100

security
No known security issues
popularity
Popular
maintenance
Healthy
community
Active

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30

Popularity

Popular

GitHub Stars: 5.55K
Forks: 1.02K
Contributors: 190

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package monai-weekly, we found that it has been starred 5,550 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Nov 3, 2024
Direct Vulnerabilities: 0
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0
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0
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0
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Indirect Vulnerabilities: 0
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0
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0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Healthy

Commit Frequency

Open Issues: 286
Open PR: 68
Last Release: 10 days ago
Last Commit: 4 months ago

Further analysis of the maintenance status of monai-weekly based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that monai-weekly demonstrates a positive version release cadence with at least one new version released in the past 3 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 190
Funding: No

A good and healthy external contribution signal for monai-weekly project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: >=3.9

Age: 4 years
Latest Release: 10 days ago
Dependencies: 0 Direct
Versions: 204
Maintainers: 1
Wheels: OS Independent

Readme

Medical Open Network for AI

Supported Python versions

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of the PyTorch Ecosystem. Its ambitions are as follows:

Developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
Creating state-of-the-art, end-to-end training workflows for healthcare imaging;
Providing researchers with the optimized and standardized way to create and evaluate deep learning models.

Features

> Please see the technical highlights and What's New of the milestone releases.

flexible pre-processing for multi-dimensional medical imaging data;
compositional & portable APIs for ease of integration in existing workflows;
domain-specific implementations for networks, losses, evaluation metrics and more;
customizable design for varying user expertise;
multi-GPU multi-node data parallelism support.

Installation

To install the current release, you can simply run:

pip install monai

Please refer to the installation guide for other installation options.

Getting Started

MedNIST demo and MONAI for PyTorch Users are available on Colab.

Examples and notebook tutorials are located at Project-MONAI/tutorials.

Technical documentation is available at docs.monai.io.

Citation

If you have used MONAI in your research, please cite us! The citation can be exported from: https://arxiv.org/abs/2211.02701.

Model Zoo

The MONAI Model Zoo is a place for researchers and data scientists to share the latest and great models from the community. Utilizing the MONAI Bundle format makes it easy to get started building workflows with MONAI.

Contributing

For guidance on making a contribution to MONAI, see the contributing guidelines.

Community

Join the conversation on Twitter/X @ProjectMONAI or join our Slack channel.

Ask and answer questions over on MONAI's GitHub Discussions tab.

FAQs

What is monai-weekly?

AI Toolkit for Healthcare Imaging. Visit Snyk Advisor to see a full health score report for monai-weekly, including popularity, security, maintenance & community analysis.

Is monai-weekly popular?

The python package monai-weekly receives a total of 10,138 weekly downloads. As such, monai-weekly popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is monai-weekly well maintained?

We found that monai-weekly demonstrated a healthy version release cadence and project activity. It has a community of 190 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is monai-weekly safe to use?

The python package monai-weekly was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 7 November-2024, at 23:23 (UTC).

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AI Toolkit for Healthcare Imaging For more information about how to use this package see README

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What is monai-weekly?

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Is monai-weekly well maintained?

Is monai-weekly safe to use?

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Package Health Score

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Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

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Snyk Vulnerability Scanner

Readme

Features

Installation

Getting Started

Citation

Model Zoo

Contributing

Community

Links

FAQs

What is monai-weekly?

Is monai-weekly popular?

Is monai-weekly well maintained?

Is monai-weekly safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

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