Hierarchical Features Integration and Attention Iteration Network for Juvenile Refractive Power Prediction

Yang Zhang, Risa Higashita, Guodong Long, Rong Li, Daisuke Santo, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Refraction power has been accredited as one of the significant indicators for the myopia detection in clinical medical practice. Standard refraction power acquirement technique based on cycloplegic autorefraction needs to induce with specific medicine lotions, which may cause side-effects and sequelae for juvenile students. Besides, several fundus lesions and ocular disorders will degenerate the performance of the objective measurement of the refraction power due to equipment limitations. To tackle these problems, we firstly propose a novel hierarchical features integration method and an attention iteration network to automatically obtain the refractive power by reasoning from relevant biomarkers. In our method, hierarchical features integration is used to generate ensembled features of different levels. Then, an end-to-end deep neural network is designed to encode the feature map in parallel and exploit an inter-scale attentive parallel module to enhance the representation through an up-bottom fusion path. The experiment results have demonstrated that the proposed approach is superior to other baselines in the refraction power prediction task, which could further be clinically deployed to assist the ophthalmologists and optometric physicians to infer the related ocular disease progression.

Original languageEnglish
Title of host publicationNeural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
EditorsTeddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030922696
Publication statusPublished - 2021
Externally publishedYes
Event28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online
Duration: 8 Dec 202112 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13109 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th International Conference on Neural Information Processing, ICONIP 2021
CityVirtual, Online


  • Attention iteration
  • Non-cycloplegic refraction records
  • Refractive power prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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