Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography.

Journal: Korean journal of radiology
Published Date: May 1, 2019

Abstract

OBJECTIVE: To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US).

Authors

  • Ji Soo Choi
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Boo Kyung Han
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. bkhan@skku.edu.
  • Eun Sook Ko
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Jung Min Bae
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Eun Young Ko
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • So Hee Song
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Mi Ri Kwon
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Jung Hee Shin
    Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Soo Yeon Hahn
    Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.

Keywords

External Resources

Popular Topics
Recent Journals