Name
Abstract | ||
|---|---|---|
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 MKL
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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 MKL
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by conducting extensive experiments for three leading ocular diseases: glaucoma, age-related macular degeneration and pathological myopia. Experimental results show that MKL
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is significantly better than the standard SVMs using data from individual domains and the traditional MKL method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISBI.2018.8363519 | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
Keywords | Field | DocType |
MKL clm,multiple informatics domains,automatic ocular disease detection,different ocular diseases,unified Multiple Kernel Learning framework,support vector machine,SVM | Informatics,Glaucoma,Disease,Pattern recognition,Computer science,Multiple kernel learning,Support vector machine,Computer aided detection,Macular degeneration,Artificial intelligence,Discriminative model | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-5386-3637-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanwu Xu | 1 | 447 | 40.32 |
| Lixin Duan | 2 | 1249 | 54.83 |
| Huazhu Fu | 3 | 1235 | 65.07 |
| Zhuo Zhang | 4 | 186 | 27.49 |
| Wei Zhao | 5 | 14 | 4.05 |
| Tianyuan You | 6 | 2 | 1.36 |
| Tien Yin Wong | 7 | 389 | 38.10 |
| Jiang Liu | 8 | 299 | 42.50 |