CycleGAN-based deep learning technique for artifact reduction in fundus photography.

Journal: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Published Date: May 2, 2020

Abstract

PURPOSE: A low quality of fundus photograph with artifacts may lead to false diagnosis. Recently, a cycle-consistent generative adversarial network (CycleGAN) has been introduced as a tool to generate images without matching paired images. Therefore, herein, we present a deep learning technique that removes the artifacts automatically in a fundus photograph using a CycleGAN model.

Authors

  • Tae Keun Yoo
  • Joon Yul Choi
    Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.
  • Hong Kyu Kim
    Department of Ophthalmology, Dankook University Hospital, Dankook University College of Medicine, Cheonan, South Korea.

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