Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application.

Abstract

Authors

• Jinyoung Kim
  R&D Center, VUNO, Seoul, Republic of Korea.
• Min-Hee Kim
  Institute of Traditional Medicine and Bioscience, Daejeon University, Daejeon, Korea.
• Dong-Jun Lim
  Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
• Hankyeol Lee
  Department of Computer Engineering, Hongik University, Seoul, Korea.
• Jae Jun Lee
  Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea.
• Hyuk-Sang Kwon
  Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
• Mee Kyoung Kim
  Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
• Ki-Ho Song
  Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.
• Tae-Jung Kim
  Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea.
• So Lyung Jung
  Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Korea.
• Yong Oh Lee
  Smart Convergence Group, KIST Europe, Saarbrücken, 66123, Germany.
• Ki-Hyun Baek
  Division of Endocrinology and Metabolism, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Keywords

External Resources