Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.

Journal: Pediatric radiology
Published Date: Apr 30, 2019

Abstract

BACKGROUND: An automated method for identifying the anatomical region of an image independent of metadata labels could improve radiologist workflow (e.g., automated hanging protocols) and help facilitate the automated curation of large medical imaging data sets for machine learning purposes. Deep learning is a potential tool for this purpose.

Authors

  • Paul H Yi
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: Pyi10@jhmi.edu.
  • Tae Kyung Kim
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA.
  • Jinchi Wei
    Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
  • Jiwon Shin
    Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA.
  • Ferdinand K Hui
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Haris I Sair
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA.
  • Gregory D Hager
    Department of Computer Science, The Johns Hopkins University, 3400 N. Charles St., Malone Hall Room 340, Baltimore, MD, 21218, USA.
  • Jan Fritz
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline St., Room 4223, Baltimore, MD, 21287, USA. jfritz9@jhmi.edu.

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