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Multiple Ocular Diseases Classification with Graph Regularized Probabilistic Multi-label Learning

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9006))

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Abstract

Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three leading ocular diseases in the world. In this paper, we proposed a multiple ocular diseases diagnosis approach for above three diseases, with Entropic Graph regularized Probabilistic Multi-label learning (EGPM). The proposed EGPM exploits the correlations among these three diseases, and simultaneously classifying them for a given fundus image. The EGPM scheme contains two concatenating parts: (1) efficient graph construction based on k-Nearest-Neighbor (k-NN) search; (2) entropic multi-label learning based on Kullback-Leibler divergence. In addition, to capture the characteristics of these three leading ocular diseases, we explore the extractions of various effective low-level features, including Global Features, Grid-based Features, and Bag of Visual Words. Extensive experiments are conducted to validate the proposed EGPM framework on SiMES dataset. The results of Area Under Curve (AUC) in multiple ocular diseases classification outperform the state-of-the-art algorithms.

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Author information

Authors and Affiliations

  1. Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore

    Xiangyu Chen, Yanwu Xu, Lixin Duan, Zhuo Zhang, Damon Wing Kee Wong & Jiang Liu

  2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Shuicheng Yan

Authors
  1. Xiangyu Chen
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  2. Yanwu Xu
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  3. Lixin Duan
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  4. Shuicheng Yan
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  5. Zhuo Zhang
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  6. Damon Wing Kee Wong
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  7. Jiang Liu
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Corresponding author

Correspondence to Xiangyu Chen .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Chen, X. et al. (2015). Multiple Ocular Diseases Classification with Graph Regularized Probabilistic Multi-label Learning. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-16817-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16816-6

  • Online ISBN: 978-3-319-16817-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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