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.
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.
You can quickly get started with
the Python Package Index (PyPI)
Or via several
alternative installation approaches
Abdominal CT segmentation with 3D UNet
Medical image segmentation tutorial
Fast training with MONAI components
Approximate 12x speedup with CacheDataset, Novograd, and AMP
Training and evaluation - classification
Brain MRI classification examples
Training and evaluation - segmentation
Volumetric image segmentation examples
Training and evaluation - MONAI workflows
Image segmentation examples with engine and event-handlers
Training and evaluation - distributed data parallel
Examples based on PyTorch, Horovod, and MONAI workflows
View a list of all
MONAI tutorials and examples
More domain-optimized networks, loss functions, metrics, and optimizers.
Components fully support distributed data parallel with rich examples.
Enabled PyTorch AMP in workflows, provided rich tutorials and examples.
New image readers and LoadImage transform for more medical image formats.
C++/CUDA optimized components and enabled CI/CD for to build.
Various tutorial notebooks and examples for new features.
Provides ready-to-use deep learning models, walk-through tutorials and examples to get kick-started for AI development on a common foundation.
For advanced academic and translational researchers, MONAI provides modular domain optimized components that can be easily integrated into existing workflows and enables reproducibility of research experiments for comparison.
Project MONAI is an initiative originally started by NVIDIA & King’s College London to establish an inclusive community of AI researchers for the development and exchange of best practices for AI in healthcare imaging across academia and enterprise researchers.