It’s never been more important to put powerful AI tools in the hands of the world’s leading medical researchers.
That’s why we’re introducing MONAI, our latest initiative with King’s College London. This open-source AI framework for healthcare builds on the best practices from existing tools, including NVIDIA Clara, NiftyNet, DLTK and DeepNeuro.
MONAI is user-friendly, delivers reproducible results and is domain-optimized for the demands of healthcare data — equipped to handle the unique formats, resolutions and specialized meta-information of medical images. Our first public release provides domain-specific data transforms, neural network architectures and evaluation methods to measure the quality of medical imaging models.
“In partnership with NVIDIA, Project MONAI is following industry standards for open-source development and building a global community across academia and industry to establish a high quality framework supporting scientific development in medical imaging AI,” said Seb Ourselin, head of the School of Biomedical Engineering & Imaging Sciences at King’s College London.
NVIDIA and King’s College London are leading the initiative in collaboration with an advisory board hailing from the Chinese Academy of Sciences, the German Cancer Research Center, Kitware, MGH & BWH Center for Clinical Data Science, Stanford University and the Technical University of Munich.
“Project MONAI has outstanding potential to accelerate the pace of medical imaging AI research,” said Stephen Aylward, chair of the MONAI advisory board and a senior director at open-source software company Kitware. “It provides a high-quality, open-source foundation that is specialized for medical imaging, that welcomes everyone to build upon, and that anyone can use to communicate and compare their ideas.”
Available on GitHub, the open-source code is based on the Ignite and PyTorch deep learning frameworks, and brings together state-of-the-art libraries for data processing, 2D classification, 3D segmentation and more. Researchers can easily bring MONAI to their existing code, using the customizable design to integrate modular components into their AI workflows.
An Open, Flexible Framework for Healthcare
Modular, open-source solutions give researchers the flexibility to customize their deep learning development, without needing to replace their existing workflows with an end-to-end system.
An advanced researcher could, for instance, adopt MONAI code for data preprocessing and transformations, and then switch over to an existing AI pipeline for training.
“Researchers need a flexible, powerful and composable framework that allows them to do innovative medical AI research, while providing the robustness, testing and documentation necessary for safe hospital deployment,” said Jorge Cardoso, chief technology officer of the London Medical Imaging & AI Centre for Value-based Healthcare. “Such a tool was missing prior to Project MONAI.”
Detailed tutorials and a user-friendly API interface allow entry-level researchers to define an end-to-end training workflow.
A key goal of the MONAI framework is to enable reproducibility of experiments, so researchers can share results and build upon each other’s work to advance the state of the art.
“Reproducibility of scientific research is of paramount importance, especially when we are talking about the application of AI in medicine,” said Jayashree Kalpathy-Cramer, scientific director at the MGH & BWH Center for Clinical Data Science, and associate professor of radiology at MGH/Harvard Medical School. “Project MONAI is providing a framework by which AI development for medical imaging can be validated and refined by the community with data and techniques from the world over.”
Future releases of NVIDIA Clara will also leverage the MONAI framework. We plan to bring together development efforts for NVIDIA Clara medical imaging tools and MONAI to continue delivering domain-optimized, robust software tools for researchers in healthcare imaging.
With contributions from an engaged community, the project will increase efficiency and collaboration among academic and industry researchers.