The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm.
Project MONAI also includes MONAI Label, an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create annotated datasets, and build AI models in a standardized MONAI paradigm.
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. MONAI provides those tools for the entire Medical AI Model development workflow, from Research to Clinical Production.