Join us for our next NVIDIA webinar to learn how you can accelerate your Medical Imaging research and clinical collaboration.

MONAI (Medical Open Network for AI) is the domain-specific, open-source Medical AI framework that drives research breakthroughs and accelerates AI for clinical impact. MONAI unites doctors with data scientists to unlock the power of medical data to build deep learning models and deployable applications for medical AI workflows. The framework provides essential domain-specific tools, from data labeling to model training to application deployment, making it easy to develop, reproduce, and standardize medical AI lifecycles.

• MONAI Label provides a clinician-friendly application to expertly label data in a fraction of the time while simultaneously training a model at the push of a button.
• MONAI Core implements state-of-the-art data transformations, model architectures, and training paradigms such as self-supervised, federated learning, and autoML.
• MONAI Deploy provides a quick, easy and standardized way to define a model specification, turn a model into an application, and run the application in a real-world clinical environment.

• How MONAI Label enables users to create annotated datasets and build AI annotation models for clinical evaluation
• How MONAI Label can help you annotate your dataset using an existing sample application
• The continuous training loop and active learning options
• MONAI Core and how it fits into the overall Medical Imaging workflow
• The basics, including transforms, dataset caching/loading, and training your first network with MONAI
• How MONAI Deploy is bridging the gap from research innovation to clinical production
• How to use the MONAI Deploy App SDK for building, verifying, and packaging a deploy-ready AI Application (MAP)