Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging.

Journal: International journal of computer assisted radiology and surgery
Published Date: Sep 23, 2020

Abstract

PURPOSE: Machine learning (ML) algorithms are well known to exhibit variations in prediction accuracy when provided with imbalanced training sets typically seen in medical imaging (MI) due to the imbalanced ratio of pathological and normal cases. This paper presents a thorough investigation of the effects of class imbalance and methods for mitigating class imbalance in ML algorithms applied to MI.

Authors

  • Wendi Qu
    Department of Medical Imaging, University of Toronto, Toronto, ON, M5T 1W7, Canada.
  • Indranil Balki
    Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
  • Mauro Mendez
    Department of Medical Imaging, University of Toronto, Toronto, ON, M5T 1W7, Canada.
  • John Valen
    Department of Medical Imaging, University of Toronto, Toronto, ON, M5T 1W7, Canada.
  • Jacob Levman
    Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical SchoolBoston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestown, MA, USA.
  • Pascal N Tyrrell
    Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada; Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada. Electronic address: pascal.tyrrell@utoronto.ca.

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