Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation.

Journal: Medical physics
Published Date:

Abstract

PURPOSE: As deep neural networks achieve more success in the wide field of computer vision, greater emphasis is being placed on the generalizations of these models for production deployment. With sufficiently large training datasets, models can typically avoid overfitting their data; however, for medical imaging it is often difficult to obtain enough data from a single site. Sharing data between institutions is also frequently nonviable or prohibited due to security measures and research compliance constraints, enforced to guard protected health information (PHI) and patient anonymity.

Authors

  • Samuel W Remedios
    Johns Hopkins University, Baltimore MD 21218, USA.
  • Snehashis Roy
    The Henry M. Jackson Foundation for the Advancement of Military Medicine, United States.
  • Camilo Bermudez
  • Mayur B Patel
    2Vanderbilt University School of Medicine, Nashville, Tennessee.
  • John A Butman
    Clinical Center, National Institutes of Health, Bethesda MD 20814, USA.
  • Bennett A Landman
    Vanderbilt University, Nashville TN 37235, USA.
  • Dzung L Pham
    Clinical Center, National Institutes of Health, Bethesda MD 20814, USA.