Intel And Penn Medicine Announce Results Of Largest Medical Federated Learning Study

What’s New: Intel Labs and the
Perelman School of Medicine at the University of
Pennsylvania (Penn Medicine) have completed
a joint research study using federated learning – a
distributed machine learning (ML) artificial intelligence
(AI) approach – to help international healthcare and
research institutions identify malignant brain tumors. The
largest medical federated learning study to date with an
unprecedented global dataset examined from 71 institutions
across six continents, the project demonstrated the ability
to improve brain tumor detection by
33%.

“Federated learning has tremendous potential
across numerous domains, particularly within healthcare, as
shown by our research with Penn Medicine. Its ability to
protect sensitive information and data opens the door for
future studies and collaboration, especially in cases where
datasets would otherwise be inaccessible. Our work with Penn
Medicine has the potential to positively impact patients
across the globe and we look forward to continuing to
explore the promise of federated learning.”
—
Jason Martin, principal engineer, Intel
Labs

Why It Matters: Data
accessibility has long been an issue in healthcare because
of state and national data privacy laws, including the
Health Insurance Portability and Accountability Act (HIPAA).
Because of this, medical research and data sharing at scale
have been almost impossible to achieve without compromising
patient health information. Intel’s federated learning
hardware and software comply with data privacy concerns and
preserve data integrity, privacy and security through
confidential computing.

The Penn Medicine-Intel result
was accomplished by processing high volumes of data in a
decentralised system using Intel federated learning
technology paired with Intel® Software Guard Extensions
(SGX), which remove data-sharing barriers that have
historically prevented collaboration on similar cancer and
disease research. The system addresses numerous data privacy
concerns by keeping raw data inside the data holders’
compute infrastructure and only allowing model updates
computed from that data to be sent to a central server or
aggregator, not the data itself.

“All of the
computing power in the world can’t do much without enough
data to analyse,” said Rob Enderle, principal analyst,
Enderle Group. “This inability to analyse data that has
already been captured has significantly delayed the massive
medical breakthroughs AI has promised. This federated
learning study showcases a viable path for AI to advance and
achieve its potential as the most powerful tool to fight our
most difficult ailments.”

“In this study,
federated learning shows its potential as a paradigm shift
in securing multi-institutional collaborations by enabling
access to the largest and most diverse dataset of
glioblastoma patients ever considered in the literature,
while all data are retained within each institution at all
times,” said senior author Spyridon
Bakas, PhD, assistant professor of Pathology &
Laboratory Medicine, and Radiology, at the Perelman School
of Medicine at the University of Pennsylvania. “The more
data we can feed into machine learning models, the more
accurate they become, which in turn can improve our ability
to understand and treat even rare diseases, such as
glioblastoma.”

To advance the treatment of diseases,
researchers must access large amounts of medical data – in
most cases, datasets that exceed the threshold that one
facility can produce. The research demonstrates the
effectiveness of federated learning at scale and the
potential benefits the healthcare industry can realise when
multisite data silos are unlocked. Benefits include early
detection of disease, which could improve quality of life or
increase a patient’s lifespan.

The results of the
Penn Medicine-Intel Labs research were published in the
peer-reviewed journal, Nature
Communications.

About the
Research: In 2020, Intel and Penn Medicine
announced the agreement
to cooperate and use federated learning to improve tumor
detection and improve treatment outcomes of a rare form of
cancer called glioblastoma (GBM), the most common and fatal
adult brain tumor with a median survival of just 14 months
after standard treatment. While treatment options have
expanded over the past 20 years, there has not been an
improvement in overall survival rates. The research was
funded by the Informatics Technology for
Cancer Research program out of the National Cancer
Institute of the National Institutes of Health.

Penn
Medicine and 71 international healthcare/research
institutions used Intel’s federated learning hardware and
software to improve the detection of rare cancer boundaries.
A new state-of-the-art AI software platform called Federated
Tumor Segmentation (FeTS) was used by radiologists to
determine the boundary of a tumor and improve the
identification of the “operable region” of tumors or
“tumor core.” Radiologists annotated their data and used
open federated learning (OpenFL), an open
source framework for training machine learning algorithms,
to run the federated training. The platform was trained on
3.7 million images from 6,314 GBM patients across six
continents, the largest brain tumor dataset to
date.

What’s Next: Through this
project, Intel Labs and Penn Medicine have created a proof
of concept for using federated learning to gain knowledge
from data. The solution can significantly affect healthcare
and other study areas, particularly among other types of
cancer research. Specifically, Intel developed the OpenFL
open source project to enable customers to adopt real-world
cross-silo federated learning and confidently deploy it on
Intel SGX. In addition, the novel FeTS initiative was
established as a collaborative network to provide a platform
for ongoing development and to encourage collaboration with
the FeTS platform and Intel’s OpenFL open source toolkit,
both available on GitHub.

More Context:
Intel
Works with the University of Pennsylvania in Using
Privacy-Preserving AI to Identify Brain Tumors | Nature
Communications Report | Intel
and Penn Medicine Announce Results of Largest Medical
Federated Learning Study (Video) | Secure
Federated Learning for a Better World (Case Study) | Intel,
Penn Medicine Federated Learning Study (Quote
Sheet)

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