Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institu...

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Vydané v:	Scientific reports Ročník 10; číslo 1; s. 12598
Hlavní autori:	Sheller, Micah J., Edwards, Brandon, Reina, G. Anthony, Martin, Jason, Pati, Sarthak, Kotrotsou, Aikaterini, Milchenko, Mikhail, Xu, Weilin, Marcus, Daniel, Colen, Rivka R., Bakas, Spyridon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 28.07.2020 Nature Publishing Group
Predmet:	631/67 631/67/1922 639/166/985 639/705/1042 639/705/1046 692/700 692/700/1421 692/700/1421/65 Collaboration Humanities and Social Sciences Humans Information Dissemination Institutions Interinstitutional Relations Learning Medicine multidisciplinary Patients Precision medicine Privacy Science Science (multidisciplinary) Training
ISSN:	2045-2322, 2045-2322
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Abstract	Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.
AbstractList	Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine. Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.
ArticleNumber	12598
Author	Edwards, Brandon Xu, Weilin Marcus, Daniel Martin, Jason Colen, Rivka R. Bakas, Spyridon Pati, Sarthak Milchenko, Mikhail Kotrotsou, Aikaterini Sheller, Micah J. Reina, G. Anthony
Author_xml	– sequence: 1 givenname: Micah J. surname: Sheller fullname: Sheller, Micah J. organization: Intel Corporation – sequence: 2 givenname: Brandon surname: Edwards fullname: Edwards, Brandon organization: Intel Corporation – sequence: 3 givenname: G. Anthony surname: Reina fullname: Reina, G. Anthony organization: Intel Corporation – sequence: 4 givenname: Jason surname: Martin fullname: Martin, Jason organization: Intel Corporation – sequence: 5 givenname: Sarthak orcidid: 0000-0003-2243-8487 surname: Pati fullname: Pati, Sarthak organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania – sequence: 6 givenname: Aikaterini orcidid: 0000-0002-0433-7159 surname: Kotrotsou fullname: Kotrotsou, Aikaterini organization: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center – sequence: 7 givenname: Mikhail surname: Milchenko fullname: Milchenko, Mikhail organization: Department of Radiology, Washington University School of Medicine – sequence: 8 givenname: Weilin surname: Xu fullname: Xu, Weilin organization: Intel Corporation – sequence: 9 givenname: Daniel orcidid: 0000-0001-9501-8104 surname: Marcus fullname: Marcus, Daniel organization: Department of Radiology, Washington University School of Medicine – sequence: 10 givenname: Rivka R. orcidid: 0000-0002-0882-0607 surname: Colen fullname: Colen, Rivka R. organization: Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Hillman Cancer Center, University of Pittsburgh Medical Center, Department of Radiology, University of Pittsburgh – sequence: 11 givenname: Spyridon orcidid: 0000-0001-8734-6482 surname: Bakas fullname: Bakas, Spyridon email: sbakas@upenn.edu organization: Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32724046$$D View this record in MEDLINE/PubMed
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Snippet	Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying...
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Title	Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
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