Differentiation of thyroid nodules on US using features learned and extracted from various convolutional neural networks.

Journal: Scientific reports
Published Date: Dec 27, 2019

Abstract

Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-invasive and can accurately differentiate benign and malignant thyroid nodules, it is subjective and its results inevitably lack reproducibility. Therefore, to provide objective and reliable information for US assessment, we developed a CADx system that utilizes convolutional neural networks and the machine learning technique. The diagnostic performances of 6 radiologists and 3 representative results obtained from the proposed CADx system were compared and analyzed.

Authors

  • Eunjung Lee
    Department of Computational Science and Engineering, Yonsei University, Seoul, Korea.
  • Heonkyu Ha
    Department of Computational Science and Engineering, Yonsei University, Seoul, South Korea.
  • Hye Jung Kim
    Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Seoul, South Korea.
  • Hee Jung Moon
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea (G.R.K., E.-K.K., J.H.Y., H.J.M., J.Y.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea (S.J.K.); Department of Radiology, Ajou University School of Medicine, Suwon, Korea (E.J.H.); Yonsei University College of Medicine, Seoul, Korea (J.Y.); and Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea (H.S.L., J.H.H.).
  • Jung Hee Byon
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea.
  • Sun Huh
    Department of Parasitology, Institute of Medical Education, College of Medicine, Hallym University, Chuncheon, Korea.
  • Jinwoo Son
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea.
  • Jiyoung Yoon
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea.
  • Kyunghwa Han
    From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea (S.H.P.); and Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.).
  • Jin Young Kwak
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea (G.R.K., E.-K.K., J.H.Y., H.J.M., J.Y.K.); Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea (S.J.K.); Department of Radiology, Ajou University School of Medicine, Suwon, Korea (E.J.H.); Yonsei University College of Medicine, Seoul, Korea (J.Y.); and Department of Research Affairs, Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea (H.S.L., J.H.H.). docjin@yuhs.ac.

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