Intel Labs and the Perelman School of Medicine (Penn Medicine) are co-developing technology to train artificial intelligence (AI) models that identify brain tumors using a privacy-preserving technique called federated learning. Penn Medicine’s work is funded by the Informatics Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI) of the National Institutes of Health (NIH). In three years, $1.2 is million grant awarded to principal investigator Dr. Spyridon Bakas at the Center for Biomedical Image Computing and Analytics (CBICA).
Jason Martin, principal engineer, Intel Labs said, “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”.
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. It is a distributed machine learning approach that enables organizations to collaborate on deep learning projects.
Dr. Spyridon Bakas said that in the scientific community machine learning training requires a lot of data and also diverse data but single institution cannot hold this alone.
He added, “We are coordinating a federation of 29 collaborating international healthcare and research institutions, which will be able to train state-of-the-art AI models for healthcare, using privacy-preserving machine learning technologies, including federated learning. This year, the federation will begin developing algorithms that identify brain tumors from a greatly expanded version of the International Brain Tumor Segmentation (BraTS) challenge dataset. This federation will allow medical researchers access to vastly greater amounts of healthcare data while protecting the security of that data”.
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