Intel Works with University of Pennsylvania in Using Privacy-Preserving AI to Identify Brain Tumors
May 11 2020 - 9:00AM
Business Wire
What’s New: Intel Labs and the Perelman School of
Medicine at the University of Pennsylvania (Penn Medicine) are
co-developing technology to enable a federation of 29 international
healthcare and research institutions led by Penn Medicine 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), through a
three-year, $1.2 million grant awarded to principal investigator
Dr. Spyridon Bakas at the Center for Biomedical Image Computing and
Analytics (CBICA) of the University of Pennsylvania.
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Federated learning is a distributed
machine learning approach that enables organizations to collaborate
on machine learning projects without sharing sensitive data such as
patient records. (Credit: Intel Corporation)
“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.” – Jason Martin, principal engineer, Intel Labs
How It Works: 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.
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 (MICCAI) 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.
“It is widely accepted by our scientific community that machine
learning training requires ample and diverse data that no single
institution can hold,” Bakas said. “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.”
Why It Matters: According to the American Brain Tumor
Association (ABTA), nearly 80,000 people will be diagnosed with a
brain tumor this year, with more than 4,600 of them being children.
In order to train and build a model to detect a brain tumor that
could aid in early detection and better outcomes, researchers need
access to large amounts of relevant medical data. However, it is
essential that the data remain private and protected, which is
where federated learning with Intel technology comes in. By
utilizing this approach, researchers from all partner organizations
will be able to work together on building and training an algorithm
to detect a brain tumor while protecting sensitive medical
data.
What’s Next: In 2020, Penn and the 29 international
healthcare and research institutions will use Intel’s federated
learning hardware and software to produce a new state-of-the-art AI
model that is trained on the largest brain tumor dataset to date —
all without sensitive patient data leaving the individual
collaborators. The subset of collaborating institutions expected to
participate in initiating the first phase of this federation
includes the Hospital of the University of Pennsylvania, Washington
University in St. Louis, the University of Pittsburgh Medical
Center, Vanderbilt University, Queen’s University, Technical
University of Munich, University of Bern, King’s College London and
Tata Memorial Hospital.
More Context: Federated Learning for Medical Imaging
(blog) | Advancing Both AI and Privacy is Not a Zero-Sum Game
(Fortune Op-Ed) | Artificial Intelligence at Intel | Federated
Learning in Medicine
About Intel
Intel (Nasdaq: INTC) is an industry leader, creating
world-changing technology that enables global progress and enriches
lives. Inspired by Moore’s Law, we continuously work to advance the
design and manufacturing of semiconductors to help address our
customers’ greatest challenges. By embedding intelligence in the
cloud, network, edge and every kind of computing device, we unleash
the potential of data to transform business and society for the
better. To learn more about Intel’s innovations, go to
newsroom.intel.com and intel.com.
© Intel Corporation. Intel, the Intel logo and other Intel marks
are trademarks of Intel Corporation or its subsidiaries. Other
names and brands may be claimed as the property of others.
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Alexa Korkos 415-706-5783 alexa.korkos@intel.com
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