Contextual Augmentation Based on Metric-Guided Features for Ocular Axial Length Prediction

Yeonwoo Jeong, Jae Ho Han, Jae Ryung Oh

Research output: Contribution to journalArticlepeer-review

Abstract

Ocular axial length (AL) measurement is important in ophthalmology because it should be considered prior to operations, such as strabismus surgery or cataract surgery, and the automation of AL measurement with easily obtained retinal fundus images has been studied. However, the performance of deep learning methods inevitably depends on distribution of the data set used, and the lack of data is an issue that needs to be addressed. In this study, we propose a framework for generating pairs of fundus images and their corresponding ALs to improve the AL inference. The generator’s encoder was trained independently using metric learning based on the AL information. A random vector and zero padding were incorporated into the generator to increase data creation flexibility, after which AL information was inserted as conditional information. We verified the effectiveness of this framework by evaluating the performance of AL inference models after training them on a combined data set comprising privately collected actual data and data generated by the proposed method. Compared to using only the actual data set, the mean absolute error and standard deviation of the proposed method decreased from 10.23 and 2.56 to 3.96 and 0.23, respectively, even with a smaller number of layers in the AL prediction models.

Original languageEnglish
Article number3021
JournalMathematics
Volume11
Issue number13
DOIs
Publication statusPublished - 2023 Jul

Bibliographical note

Funding Information:
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-RS-2022-00156225) and under the ICT Creative Consilience program (IITP-2023-2020-0-01819) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • axial length
  • data augmentation
  • data generation
  • deep learning
  • fundus image

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Mathematics
  • Engineering (miscellaneous)

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