Exploiting Active Learning in Novel Refractive Error Detection with Smartphones

Yujun Fu, Zhongqi Yang, Hong Va Leong, Grace Ngai, Chi Wai Do, Lily Yee Lai Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Refractive errors, such as myopia and astigmatism, can lead to severe visual impairment if not detected and corrected in time. Traditional methods of refractive error diagnosis rely on well-trained optometrists operating expensive and importable devices, constraining the vision screening process. Advance in smartphone camera has enabled novel low-cost ubiquitous vision screening to detect refractive error or ametropia through eye image processing, based on the principle of photorefraction. However, contemporary smartphone-based methods rely heavily on hand-crafted features and sufficiency of well-labeled data. To address these challenges, this paper exploits active learning methods with a set of Convolutional Neural Network features encoding information of human eyes from pre-trained gaze estimation model. This enables more effective training on refractive error detection models with less labeled data. Our experimental results demonstrate the encouraging effectiveness of our active learning approach. The new set of features is able to attain screening accuracy of more than 80% with mean absolute error less than 0.66, meeting the expectation of optometrists for 0.5 to 1. The proposed active learning also requires significantly fewer training samples of 18% in achieving satisfactory performance.
Original languageEnglish
Title of host publicationMM '20: Proceedings of the 28th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery
Pages2775-2783
Number of pages9
ISBN (Electronic)9781450379885
DOIs
Publication statusPublished - 12 Oct 2020
EventMM '20: The 28th ACM International Conference on Multimedia - Virtual, Online, United States
Duration: 12 Oct 202016 Oct 2020
https://dl.acm.org/doi/proceedings/10.1145/3394171

Competition

CompetitionMM '20: The 28th ACM International Conference on Multimedia
Country/TerritoryUnited States
Period12/10/2016/10/20
Internet address

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