Intel and Penn Medicine Work in Using AI to Identify Brain Tumors
Intel Labs and the Perelman School of Medicine at the University of Pennsylvania are co-developing technology to enable 29 international healthcare and research institutions to train AI models that identify brain tumors using a privacy-preserving technique called federated learning.
Penn Medicine and 29 healthcare and research institutions from the United States, Canada, the United Kingdom, Germany, the Netherlands, Switzerland and India will use federated learning, which is a distributed machine learning approach that enables organizations to collaborate on deep learning projects without sharing patient data.
“AI shows great promise for the early detection of brain tumors, but it will require more data than any single medical center holds to reach its full potential. Using Intel software and hardware and support from some of Intel Labs’ brightest minds, we are working with the University of Pennsylvania and a federation of 29 collaborating medical centers to advance the identification of brain tumors while protecting sensitive patient data,” said Jason Martin, principal engineer at Intel Labs.
Penn Medicine and Intel Labs were the first to publish a paper on federated learning in the medical imaging domain, particularly demonstrating that the federated learning method could train a model to over 99% of the accuracy of a model trained in the traditional, non-private method. This paper was originally presented at the International Conference on Medical Image Computing and Computer Assisted Intervention 2018 in Granada, Spain. The new work will leverage Intel software and hardware to implement federated learning in a manner that provides additional privacy protection to both the model and the data.
In 2020, Penn and the 29 international healthcare and research institutions will use Intel’s federated learning hardware and software to produce a new AI model that is trained on the largest brain tumor dataset to date. All work will be conducted without sensitive patient data leaving the individual collaborators.