Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
dc.contributor.author | Huck Yang, C. H. | |
dc.contributor.author | Liu, Fangyu | |
dc.contributor.author | Huang, Jia-Hong | |
dc.contributor.author | Tian, Meng | |
dc.contributor.author | I-Hung Lin, M. D. | |
dc.contributor.author | Liu, Yi Chieh | |
dc.contributor.author | Morikawa, Hiromasa | |
dc.contributor.author | Yang, Hao Hsiang | |
dc.contributor.author | Tegner, Jesper | |
dc.date.accessioned | 2019-07-25T13:39:54Z | |
dc.date.available | 2019-07-25T13:39:54Z | |
dc.date.issued | 2019-06-19 | |
dc.identifier.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_28 | |
dc.identifier.doi | 10.1007/978-3-030-21074-8_28 | |
dc.identifier.uri | http://hdl.handle.net/10754/656187 | |
dc.description.abstract | 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). | |
dc.publisher | Springer Nature | |
dc.relation.url | http://link.springer.com/10.1007/978-3-030-21074-8_28 | |
dc.rights | The final publication is available at Springer via 10.1007/978-3-030-21074-8_28 | |
dc.rights.uri | http://creativecommons.org/licenses/by-sa/4.0/ | |
dc.title | Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model | |
dc.type | Conference Paper | |
dc.contributor.department | Biological and Environmental Sciences and Engineering (BESE) Division | |
dc.contributor.department | Bioscience | |
dc.contributor.department | Bioscience Program | |
dc.contributor.department | Earth Science and Engineering | |
dc.contributor.department | Earth Science and Engineering Program | |
dc.conference.date | 2018-12-02 to 2018-12-06 | |
dc.conference.name | 14th Asian Conference on Computer Vision, ACCV 2018 | |
dc.conference.location | Perth, WA, AUS | |
dc.eprint.version | Pre-print | |
dc.contributor.institution | Georgia Institute of Technology, Atlanta, GA, USA | |
dc.contributor.institution | University of Waterloo, Waterloo, Canada | |
dc.contributor.institution | National Taiwan University, Taipei, Taiwan | |
dc.contributor.institution | Department of Ophthalmology, Bern University Hospital, Bern, Switzerland | |
dc.contributor.institution | Department of Ophthalmology, Tri-Service General Hospital, Taipei, Taiwan | |
dc.contributor.institution | Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Sweden | |
dc.identifier.arxivid | 1808.05754 | |
kaust.person | Huck Yang, C. H. | |
kaust.person | Huang, Jia-Hong | |
kaust.person | Morikawa, Hiromasa | |
kaust.person | Tegner, Jesper | |
dc.relation.issupplementedby | github:huckiyang/EyeNet2 | |
refterms.dateFOA | 2019-12-01T13:47:24Z | |
display.relations | <b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> 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: <a href="https://github.com/huckiyang/EyeNet2" >huckiyang/EyeNet2</a> Handle: <a href="http://hdl.handle.net/10754/668078" >10754/668078</a></a></li></ul> | |
dc.date.published-online | 2019-06-19 | |
dc.date.published-print | 2019 | |
dc.date.posted | 2018-11-01 |
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