Ocular disease detection from multiple informatics domains

Yanwu Xu, Lixin Duan, Huazhu Fu, Zhuo Zhang, Wei Zhao, Tianyuan You, Tien Yin Wong, Jiang Liu

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

3 Citations (Scopus)


Computer aided detection for automatic ocular disease detection is an important area of research. As different ocular diseases possess different characteristics and present at different locations within the eye, it is difficult to find a common way to effectively handle each ocular disease. To solve this problem, we propose a unified Multiple Kernel Learning framework called MKLclm to detect ocular diseases, based on the existence of multiple informatics domains. Our framework is capable to learn a robust predictive model by effectively integrating discriminative knowledge from different informatics domains and incorporating pre-learned Support Vector Machine (SVM) classifiers simultaneously. We validate MKLclm by conducting extensive experiments for three leading ocular diseases: glaucoma, age-related macular degeneration and pathological myopia. Experimental results show that MKLclm is significantly better than the standard SVMs using data from individual domains and the traditional MKL method.

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538636367
Publication statusPublished - 23 May 2018
Externally publishedYes
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: 4 Apr 20187 Apr 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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