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

Yeonwoo Jeong, Jae-Ho Han () and Jaeryung Oh
Additional contact information
Yeonwoo Jeong: Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
Jae-Ho Han: Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
Jaeryung Oh: Department of Ophthalmology, Korea University, Seoul 02841, Republic of Korea

Mathematics, 2023, vol. 11, issue 13, 1-20

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.

Keywords: axial length; data augmentation; data generation; deep learning; fundus image (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/13/3021/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/13/3021/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:13:p:3021-:d:1188929

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().