Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 25, 2020

Abstract

BACKGROUND AND OBJECTIVES: Automated breast ultrasound (ABUS) is a widely used screening modality for breast cancer detection and diagnosis. In this study, an effective and fast computer-aided detection (CADe) system based on a 3-D convolutional neural network (CNN) is proposed as the second reader for the physician in order to decrease the reviewing time and misdetection rate.

Authors

  • Woo Kyung Moon
    Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
  • Yao-Sian Huang
  • Chin-Hua Hsu
    Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Ting-Yin Chang Chien
    Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Jung Min Chang
    Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.
  • Su Hyun Lee
    Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, South Korea.
  • Chiun-Sheng Huang
    Department of Surgery, National Taiwan University Hospital, Taipei 10617, Taiwan.
  • Ruey-Feng Chang
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.

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