New machine learning method allows hospitals to share patient data -
- privately
Date:
July 28, 2020
Source:
University of Pennsylvania School of Medicine
Summary:
Researchers have shown that an approach called federated learning is
successful in the context of brain imaging, by being able to analyze
magnetic resonance imaging (MRI) scans of brain tumor patients
and distinguish healthy brain tissue from cancerous regions.
FULL STORY ==========================================================================
To answer medical questions that can be applied to a wide patient
population, machine learning models rely on large, diverse datasets
from a variety of institutions. However, health systems and hospitals
are often resistant to sharing patient data, due to legal, privacy,
and cultural challenges.
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An emerging technique called federated learning is a solution to this
dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of
Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania.
Federated learning -- an approach first implemented by Google for
keyboards' autocorrect functionality -- trains an algorithm across
multiple decentralized devices or servers holding local data samples,
without exchanging them. While the approach could potentially be used to
answer many different medical questions, Penn Medicine researchers have
shown that federated learning is successful specifically in the context
of brain imaging, by being able to analyze magnetic resonance imaging
(MRI) scans of brain tumor patients and distinguish healthy brain tissue
from cancerous regions.
A model trained at Penn Medicine, for example, can be distributed to
hospitals around the world. Doctors can then train on top of this shared
model, by inputting their own patient brain scans. Their new model will
then be transferred to a centralized server. The models will eventually
be reconciled into a consensus model that has gained knowledge from each
of the hospitals, and is therefore clinically useful.
"The more data the computational model sees, the better it learns the
problem, and the better it can address the question that it was designed
to answer," Bakas said. "Traditionally, machine learning has used data
from a single institution, and then it became apparent that those models
do not perform or generalize well on data from other institutions."
The federated learning model will need to be validated and approved by
the U.S.
Food and Drug Administration before it can be licensed and commercialized
as a clinical tool for physicians. But if and when the model is
commercialized, it would help radiologists, radiation oncologists,
and neurosurgeons make important decisions about patient care, Bakas
said. Nearly 80,000 people will be diagnosed with a brain tumor this year, according to the American Brain Tumor Association.
========================================================================== "Studies have shown that, when it comes to tumor boundaries, not only
can different physicians have different opinions, but the same physician assessing the same scan can see different tumor boundary definition on
one day of the week versus the next," he said. "Artificial Intelligence
allows a physician to have more precise information about where a tumor
ends, which directly affects a patient's treatment and prognosis."
To test the effectiveness of federated learning and compare it to other
machine learning methods, Bakas collaborated with researchers from the University of Texas MD Anderson Cancer Center, Washington University,
and the Hillman Cancer Center at the University of Pittsburgh, while
Intel Corporation contributed privacy-protecting software to the project.
The study began with a model that was pre-trained on multi-institutional
data from an open-source repository known as the International Brain Tumor Segmentation, or BraTS, challenge. BraTS currently provides a dataset that includes more than 2,600 brain scans captured with magnetic resonance
imaging (MRI) from 660 patients. Next, 10 hospitals participated in the
study by training AI models with their own patient data. The federated
learning technique was then used to aggregate the data and create the
consensus model.
The researchers compared federated learning to models trained by single institutions, as well as to other collaborative-learning approaches. The effectiveness of each method was measured by testing them against
scans that were annotated manually by neurologists. When compared to
a model trained with centralized data that did not protect patient
privacy, federated learning was able to perform almost (99 percent) identically. The findings also indicated that increased access to data
through data private, multi-institutional collaborations can benefit
model performance.
The findings from this study have paved the way for a much larger,
ambitious collaboration between Penn Medicine, Intel, and 30 partner institutions, supported by a $1.2 million grant from the National Cancer Institute of the National Institutes of Health that was awarded to Bakas earlier this year.
Intel announced in May that Bakas will lead the project, in which the
30 institutions, across nine countries, will use the federated learning approach to train a consensus AI model on brain tumor data. The final goal
of the project will be to create an open-source tool for any clinician
at any hospital to use. The development of the tool in Penn's Center for Biomedical Image Computing & Analytics (CBICA) is being led by senior
software developer Sarthak Pati, MS.
Study co-author Rivka Colen, MD, an associate professor of Radiology at
the University of Pittsburgh School of Medicine, said that this paper
and the larger federated learning project open up possibilities for even
more uses of Artificial Intelligence in health care.
"I think it's a huge game changer," Colen said. "Radiomics is to radiology
what genomics was to pathology. AI will revolutionize this field, because, right now, as a radiologist, most of what we do is descriptive. With
deep learning, we're able to extract information that is hidden in this
layer of digitized images." Additional authors on this paper include:
Micah J Sheller, Brandon Edwards G Anthony Reina, Jason Martin, Aikaterini Kotrotsou, Mikhail Milchenko, Weilin Xu, and Daniel Marcus.
========================================================================== Story Source: Materials provided by University_of_Pennsylvania_School_of_Medicine. Note: Content may be
edited for style and length.
========================================================================== Journal Reference:
1. Micah J. Sheller, Brandon Edwards, G. Anthony Reina, Jason Martin,
Sarthak Pati, Aikaterini Kotrotsou, Mikhail Milchenko, Weilin Xu,
Daniel Marcus, Rivka R. Colen, Spyridon Bakas. Federated learning
in medicine: facilitating multi-institutional collaborations
without sharing patient data. Scientific Reports, 2020; 10 (1)
DOI: 10.1038/s41598-020-69250-1 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2020/07/200728113537.htm
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