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Data Augmentation for Diabetic Retinopathy Grading in Fundus Images

안저 영상에서 당뇨병 망막병증 등급을 위한 data augmentation

  • Pham, Van-Nguyen (Department of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Choo, Hyunseung (Department of Electrical and Computer Engineering, Sungkyunkwan University)
  • ;
  • 추현승 (성균관대학교 전자전기컴퓨터공학과)
  • Published : 2022.11.21

Abstract

Diabetic retinopathy (DR) is one of the leading diseases causing vision loss. Early detection of this disease has a crucial role in protecting patients' eyes. Recent works have achieved impressive result when performing DR detection on fundus images using deep learning. In the deep learning-based approach, data augmentation has significant impact on the result. Recently, many data augmentation policies have been proposed and achieved state-of-the-art performance on different tasks. In this work, we compare effects of three data augmentation policies on DR grading in fundus images.

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Acknowledgement

This work was supported by the BK21 FOUR Project, the MSIT(Ministry of Science and ICT), Korea, under the ICT Creative Consilience program(IITP-2022-2020-0-01821) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation), the MSIT, under the Grand Information Technology Research Center support program (IITP-2022-2015-0-00742) supervised by the IITP, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2C2008447), and IITP grant funded by the Korea government (MSIT) (No.2021-0-02068, Artificial Intelligence Innovation Hub).