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.
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“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.
