Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
Type
Conference PaperAuthors
Huck Yang, C. H.Liu, Fangyu
Huang, Jia-Hong

Tian, Meng
I-Hung Lin, M. D.
Liu, Yi Chieh
Morikawa, Hiromasa
Yang, Hao Hsiang
Tegner, Jesper

KAUST Department
Biological and Environmental Sciences and Engineering (BESE) DivisionBioscience
Bioscience Program
Earth Science and Engineering
Earth Science and Engineering Program
Date
2019-06-19Preprint Posting Date
2018-11-01Online Publication Date
2019-06-19Print Publication Date
2019Permanent link to this record
http://hdl.handle.net/10754/656187
Metadata
Show full item recordAbstract
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina labels collection sorted by the professional ophthalmologist from the educational project Retina Image Bank, called EyeNet, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet, our model achieves 90.40% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists (https://github.com/huckiyang/EyeNet2).Citation
Huck Yang, C.-H., Liu, F., Huang, J.-H., Tian, M., I-Hung Lin, M. D., Liu, Y. C., … Tegnèr, J. (2019). Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model. Lecture Notes in Computer Science, 323–338. doi:10.1007/978-3-030-21074-8_28Publisher
Springer NatureConference/Event name
14th Asian Conference on Computer Vision, ACCV 2018arXiv
1808.05754Additional Links
http://link.springer.com/10.1007/978-3-030-21074-8_28Relations
Is Supplemented By:- [Software]
Title: huckiyang/EyeNet2: ACCV 18 - Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model. Publication Date: 2018-07-02. github: huckiyang/EyeNet2 Handle: 10754/668078
ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-21074-8_28
Scopus Count
Except where otherwise noted, this item's license is described as The final publication is available at Springer via 10.1007/978-3-030-21074-8_28