Optimal number of strong labels for curriculum learning with convolutional neural network to classify pulmonary abnormalities in chest radiographs.

Abstract

Authors

• Yongwon Cho
  Department of Convergence Medicine, Asan Medical Center, College of Medicine, University of Ulsan, 88, Olympic-ro 43-gil, Seoul, 05505, South Korea.
• Beomhee Park
  Kakao, Seoul, South Korea.
• Sang Min Lee
  Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
• Kyung Hee Lee
  Department of Radiology, Seoul National College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea (H.C., S.H.Y., S.J.P., C.M.P., J.H.L., H. Kim, E.J.H., S.J.Y., J.G.N., C.H.L., J.M.G.); CHESS Center, The First Hospital of Lanzhou University, Lanzhou, China (Q.X., J.L.); Department of Radiology, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (K.H.L.); Department of Internal Medicine, Incheon Medical Center, Incheon, Korea (J.Y.K.); Department of Radiology, Seoul Medical Center, Seoul, Korea (Y.K.L.); Department of Radiology, National Medical Center, Seoul, Korea (H. Ko); Department of Radiology, Myongji Hospital, Gyeonggi-do, Korea (K.H.K.); and Department of Radiology, Chonnam National University Hospital, Gwanju, Korea (Y.H.K.).
• Joon Beom Seo
  Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
• Namkug Kim
  Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Keywords

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