Distributed deep learning networks among institutions for medical imaging.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Aug 1, 2018

Abstract

OBJECTIVE: Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data.

Authors

  • Ken Chang
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Niranjan Balachandar
    Department of Radiology and Biomedical Data Science, Stanford University, Palo Alto, CA, 94305, USA.
  • Carson Lam
    Department of Biomedical Data Science, Stanford University, Stanford, California, United States.
  • Darvin Yi
    Stanford University, Department of Radiology, Stanford, CA.
  • James Brown
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
  • Andrew Beers
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Bruce Rosen
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.
  • Jayashree Kalpathy-Cramer
    Department of Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts.

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