Medical Open Network for AI

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.

Open Source

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.

User Friendly

Providing user-comprehensible error messages and easy to program API interfaces.


Provides reproducibility of research experiments for comparisons against state-of-the-art implementations.

Ease of 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.

Getting Started

User Guides

MedNIST image classification
Getting started with a GPU powered Colab tutorial

Abdominal CT segmentation with 3D UNet
Medical image segmentation tutorial

Histology image transformation demo
2D transformation demo

Volumetric image transformation demo
3D transformation tutorial

Accelerate model training by caching the preprocessors
Demo on the caching dataset

Data-parallel training with multiple-GPUs
Multi-GPU training demo

GPU-based preprocessing in native PyTorch
GPU-based pipeline demo

Build a segmentation workflow (with PyTorch Ignite)
Segmentation workflow demo

Build a segmentation workflow (with PyTorch Lightning)
Segmentation workflow demo

Developer's guide on MONAI transformations
Concepts of the MONAI transformations

Install monai

You can quickly get started with the Python Package Index (PyPI) or get the latest features via several alternative installation approaches.


Training and evaluation code examples for medical image classification.
Brain MRI classification examples

Training and evaluation code examples for medical image classification (with PyTorch Ignite).
Brain MRI classification examples with Ignite

Training and evaluation code examples for medical image segmentation.
Volumetric image segmentation examples

Training and evaluation code examples for medical image segmentation (with PyTorch Ignite).
Volumetric image segmentation examples with Ignite

Features of Public Alpha 0.1.0

Customizable design for varying user expertise to interact with the package.

Flexible pre-processing for multi-dimensional medical imaging data.

Medical Imaging optimized reference implementations for networks, losses, validation metrics, etc.

Compositional & portable enabling ease of integration in existing workflows.

Object-oriented Pythonic programming paradigm.

Multi-GPU and data parallel processing supported.

User Value Proposition

Entry Level Researcher

Provides ready-to-use deep learning models, walk-through tutorials and examples to get kick-started for AI development on a common foundation.

Advanced Researcher

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.

Repository and Roadmap

Project MONAI

GitHub Repository
Go to repository

Contributing Guidelines
Go to guide

Licensing Guide
Go to Apache 2.0 license

Bug reports and feature requests
Go to issues

Initial Roadmap Schedule

Release Roadmap APR 2020 JUL 2020 OCT 2020 0.1.0 0.2.0 0.3.0


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.

Join Project MONAI