Alexis is director of content at AIMed, with responsibility for the research, development and delivery of products across events, digital and publishing. A highly experienced events executive with a career focus on the intersection between healthcare and technology, he is also a school governor leading on teaching, learning, and quality of education.
New open-source software provides a common computing foundation for federated learning, accelerating AI in healthcare.
NVIDIA is making it easier than ever for researchers to harness federated learning by open-sourcing NVIDIA FLARE, a software development kit that helps distributed parties collaborate to develop more generalizable AI models.
Federated learning is a privacy-preserving technique that’s particularly beneficial in cases where data is sparse, confidential or lacks diversity. But it’s also useful for large datasets, which can be biased by an organization’s data collection methods, or by patient or customer demographics.
NVIDIA FLARE — short for Federated Learning Application Runtime Environment — is the engine underlying NVIDIA Clara Train’s federated learning software, which has been used for AI applications in medical imaging, genetic analysis, oncology and COVID-19 research. The SDK allows researchers and data scientists to adapt their existing machine learning and deep learning workflows to a distributed paradigm. Making ths open source will better empower cutting-edge AI in almost any industry by giving researchers and platform developers more tools to customize their federated learning solutions.
Flexible federated learning architectures for multiple industries
Federated learning participants work together to train or evaluate AI models without having to pool or exchange each group’s proprietary datasets. NVIDIA FLARE provides different distributed architectures that accomplish this, including peer-to-peer, cyclic and server-client approaches, among others.
Using the server-client technique, where learned model parameters from each participant are sent to a common server and aggregated into a global model, NVIDIA has led federated learning projects that help segment pancreatic tumors, classify breast density in mammograms to inform breast cancer risk, and predict oxygen needs for COVID patients.
The server-client architecture was also used for two federated learning collaborations using NVIDIA FLARE: NVIDIA worked with Roche Digital Pathology researchers to run a successful internal simulation using whole slide images for classification, and with Netherlands-based Erasmus Medical Center for an AI application that identifies genetic variants associated with schizophrenia cases.
Integration with healthcare AI platforms
NVIDIA FLARE can integrate with existing AI initiatives, including the open-source MONAI framework for medical imaging.
Dr Jayashree Kalapathy, associate professor of radiology at Harvard Medical School and leader of the MONAI community’s federated learning working group, said:
“Open-sourcing NVIDIA FLARE to accelerate federated learning research is especially important in the healthcare sector, where access to multi-institutional datasets is crucial, yet concerns around patient privacy can limit the ability to share data. We are excited to contribute to NVIDIA FLARE and continue the integration with MONAI to push the frontiers of medical imaging research.”
NVIDIA FLARE will also be used to power federated learning solutions at:
- American College of Radiology (ACR): The medical society has worked with NVIDIA on federated learning studies that apply AI to radiology images for breast cancer and COVID-19 applications. It plans to distribute NVIDIA FLARE in the ACR AI-LAB, a software platform that is available to the society’s tens of thousands of members.
- Flywheel: The company’s Flywheel Exchange platform enables users to access and share data and algorithms for biomedical research, manage federated projects for analysis and training, and choose their preferred federated learning solution — including NVIDIA FLARE.
- Taiwan Web Service Corporation: The company offers a GPU-powered MLOps platform that enables customers to run federated learning based on NVIDIA FLARE. Five medical imaging projects are currently being conducted on the company’s private cluster, each with several participating hospitals.
- Rhino Health: The partner and member of the NVIDIA Inception program has integrated NVIDIA FLARE into its federated learning solution, which is helping researchers at Massachusetts General Hospital develop an AI model that more accurately diagnoses brain aneurysms, and experts at the National Cancer Institute’s Early Detection Research Network develop and validate medical imaging AI models that identify early signs of pancreatic cancer.
Dr Ittai Dayan, founder of Rhino Health, said:
“To collaborate effectively and efficiently, healthcare researchers need a common platform for AI development without the risk of breaching patient privacy. Rhino Health’s ‘Federated Learning as a Platform’ solution, built with NVIDIA FLARE, will be a useful tool to help accelerate the impact of healthcare AI.”