Medical Centers Tap AI, Federated Learning For Better Cancer Detection
NVIDIA News, Friday, September 20th, 2024
A committee of experts from top U.S. medical centers and research institutes is harnessing NVIDIA-powered federated learning to evaluate the impact of federated learning and AI-assisted annotation to train AI models for tumor segmentation.
Federated learning is a technique for developing more accurate, generalizable AI models trained on data across diverse data sources without mitigating data security or privacy. It allows several organizations to collaborate on the development of an AI model without sensitive data ever leaving their servers.
'Due to privacy and data management constraints, it's growing more and more complicated to share data from site to site and aggregate it in one place - and imaging AI is developing faster than research institutes can set up data-sharing contracts,' said John Garrett, associate professor of radiology at the University of Wisconsin-Madison. 'Adopting federated learning to build and test models at multiple sites at once is the only way, practically speaking, to keep up. It's an indispensable tool.'