https://monai.io

MONAI - HomeMONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging.MONAI - Home Home Frameworks MONAI Label MONAI Core MONAI Deploy Docs MONAI Label Docs MONAI Core Docs MONAI Deploy Docs Resources About Us Getting Started Community Blog Model Zoo GitHub Medical Open Network for Artificial Intelligence Core Label Deploy App SDK Project MONAI is 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. Open Source Design Project MONAI is an open-source project. It is built on top of PyTorch and is released under the Apache 2.0 license. Standardized Aiming to capture best practices of AI development for healthcare researchers, with an immediate focus on medical imaging. User Friendly Providing user-comprehensible error messages and easy to program API interfaces. Reproducible Provides reproducibility of research experiments for comparisons against state-of-the-art implementations. Easy Integration Designed to be compatible with existing efforts and ease of 3rd party integration for various components. High Quality Delivering high-quality software with enterprise-grade development, tutorials for getting started and robust validation & documentation. Medical AI LifeCycle 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. 01. MONAI Label 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. Learn More 02. MONAI Core 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. Learn More 03. MONAI Deploy 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. Learn More Contributors 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. Join Project MONAI Get Started with Project MONAI Here are a few different paths that you can get started with depending on your workload. Annotating Images with DeepEdit and 3D Slicer 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. Read More Transformer-Based Medical Architectures 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. Read More Build your First Medical AI Application 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. Read More MONAI News Learn more about what’s happening in the MONAI Community today! Blogs and Articles Rapid Deployment of MONAI Application Packages (MAPs) in Radiology Workflows using the “mercure” Open-Source DICOM Orchestrator Simplifying 3D Medical Imaging with MONAI Auto3DSeg Upcoming and Recent Events MONAI MIDL Meetup 2023 MONAI Bootcamp 2023 MONAI Label Workshop at Project Week 38 Social Media, YouTube, and Slack 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. About Us Get Started Community Blog Docs GitHubenen1726830601https://monai.io

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