“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.
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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.
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