Convolutional neural network -based phantom image scoring for mammography quality control.

Journal: BMC medical imaging
Published Date: Dec 7, 2022

Abstract

BACKGROUND: Visual evaluation of phantom images is an important, but time-consuming part of mammography quality control (QC). Consistent scoring of phantom images over the device's lifetime is highly desirable. Recently, convolutional neural networks (CNNs) have been applied to a wide range of image classification problems, performing with a high accuracy. The purpose of this study was to automate mammography QC phantom scoring task by training CNN models to mimic a human reviewer.

Authors

  • Veli-Matti Sundell
    Department of Physics, University of Helsinki, P.O. Box 64, 00014, Helsinki, Finland. veli-matti.sundell@helsinki.fi.
  • Teemu Mäkelä
    HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00029 HUS, Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland.
  • Anne-Mari Vitikainen
    HUS Diagnostic Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, Haartmaninkatu 4, 00290, Helsinki, Finland.
  • Touko Kaasalainen
    HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.

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