Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 40037789

Abstract

OBJECTIVE: This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while maintaining high diagnostic accuracy.

Authors

  • Sungho Shim
    Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Republic of Korea.
  • Min-Soo Kim
  • Che Gyem Yae
    Department of Ophthalmology, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea.
  • Yong Koo Kang
    Department of Ophthalmology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
  • Jae Rock Do
    Department of Ophthalmology, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea.
  • Hong Kyun Kim
    Department of Ophthalmology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
  • Hyun-Lim Yang
    Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, Republic of Korea.

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