• New machine learning method allows hospi

    From ScienceDaily@1337:3/111 to All on Tue Jul 28 21:30:26 2020
    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.


    ==========================================================================
    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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