Medical Open Network
for Artificial Intelligence
a set of open-source, freely available collaborative frameworks built for accelerating research and clinical collaboration in Medical Imaging. The goal is to accelerate the pace of innovation and clinical translation by building a robust software framework that benefits nearly every level of medical imaging, deep learning research, and deployment.
Project MONAI is an open-source project. It is built on top of PyTorch and is released under the Apache 2.0 license.
Aiming to capture best practices of AI development for healthcare researchers, with an immediate focus on medical imaging.
Providing user-comprehensible error messages and easy to program API interfaces.
Provides reproducibility of research experiments for comparisons against state-of-the-art implementations.
Designed to be compatible with existing efforts and ease of 3rd party integration for various components.
Delivering high-quality software with enterprise-grade development, tutorials for getting started and robust validation & documentation.
When dealing with Medical AI, it's important to have tools that cover the end-to-end workflow. Project MONAI provides those tools for the entire Medical AI Model development workflow, from Research to Clinical Production.
MONAI Label is an intelligent image labeling and learning tool that uses AI assistance to reduce the time and effort of annotating new datasets. By utilizing user interactions, MONAI Label trains an AI model for a specific task and continuously learns and updates that model as it receives additional annotated images.
MONAI Core is the flagship library of Project MONAI and provides domain-specific capabilities for training AI models for healthcare imaging. These capabilities include medical-specific image transforms, state-of-the-art transformer-based 3D Segmentation algorithms like UNETR, and an AutoML framework named DiNTS.
MONAI Deploy aims to become the de-facto standard for developing packaging, testing, deploying, and running medical AI applications in clinical production. MONAI Deploy creates a set of intermediate steps where researchers and physicians can build confidence in the techniques and approaches used with AI — allowing for an iterative workflow.
Over the last three years our community has expanded rapidly! But it takes a community to build out the success of Project MONAI, which is why we want to highlight contributing organizations. Below, you’ll find contributors organizations who have dedicated resources to actively contributing back to Project MONAI.
Here are a few different paths that you can get started with depending on your workload.
Start by learning how to install and run the MONAI Label Server. Then utlizing 3D Slicer and the DeepEdit algorithm to annotate your images and create your AI Annotation Model.
MONAI Core has two state-of-the-art transformer based architectures specific to Medical Imaging. Get hands-on experience with using these networks following our tutorials.
Utilize MONAI Deploy App SDK to build your first AI application. Walk through the steps of creating operators for specific functions, and then utilize docker to create your portable AI container.
Learn more about what’s happening in the MONAI Community today!
Blogs and Articles
Upcoming and Recent Events
If you're looking to keep up with us on social media, we're active on Twitter at @ProjectMONAI, Medium at @monai, and our YouTube Channel Project-MONAI.
You'll find our latest blog post and updates about the newest features in Project MONAI on both of those platforms. On our YouTube channel you'll find overview videos of the MONAI Frameworks, Bootcamp and Event recordings, and we're starting a hands-on walkthrough series. It's a great if you're just getting started and want to learn more about Project MONAI.
If you're looking to get involved in the community directly, join our Slack! You can interact with the core development team and community members. For an invite to our Slack channel, please fill out our Google Form, and we'll send you an invite.
