Multiple ocular diseases classification with graph regularized probabilistic multi-label learning

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

Research output: Journal PublicationConference articlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)127-142
Number of pages16
JournalLecture Notes in Computer Science
Publication statusPublished - 2015
Externally publishedYes
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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