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Incorporating Privileged Genetic Information for Fundus Image Based Glaucoma Detection

  • Lixin Duan
  • Yanwu Xu
  • Wen Li
  • Lin Chen
  • Damon Wing Kee Wong
  • Tien Yin Wong
  • Jiang Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Visual features extracted from retinal fundus images have been increasingly used for glaucoma detection, as those images are generally easy to acquire. In recent years, genetic researchers have found that some single nucleic polymorphisms (SNPs) play important roles in the manifestation of glaucoma and also show superiority over fundus images for glaucoma detection. In this work, we propose to use the SNPs to form the so-called privileged information and deal with a practical problem where both fundus images and privileged genetic information exist for the training subjects, while the test objects only have fundus images. To solve this problem, we present an effective approach based on the learning using privileged information (LUPI) paradigm to train a predictive model for the image visual features. Extensive experiments demonstrate the usefulness of our approach in incorporating genetic information for fundus image based glaucoma detection.

Keywords

Support Vector Machine Visual Feature Test Subject Fundus Image Training Subject 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lixin Duan
    • 1
  • Yanwu Xu
    • 1
  • Wen Li
    • 2
  • Lin Chen
    • 1
  • Damon Wing Kee Wong
    • 1
  • Tien Yin Wong
    • 3
  • Jiang Liu
    • 1
  1. 1.Institute for Infocomm ResearchSingapore
  2. 2.Nanyang Technological UniversitySingapore
  3. 3.National University of SingaporeSingapore

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