Multiple ocular diseases detection based on joint sparse multi-task learning

Xiangyu Chen; Yanwu Xu; Fengshou Yin; Zhuo Zhang; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu

doi:10.1109/EMBC.2015.7319578

Multiple ocular diseases detection based on joint sparse multi-task learning

Xiangyu Chen
, Yanwu Xu
, Fengshou Yin
, Zhuo Zhang
, Damon Wing Kee Wong
, Tien Yin Wong
, Jiang Liu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

In this paper, we present a multiple ocular diseases detection scheme based on joint sparse multi-task learning. Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three major causes of vision impairment and blindness worldwide. The proposed joint sparse multitask learning framework aims to reconstruct a test fundus image with multiple features from as few training subjects as possible. The linear version of this problem could be casted into a multi-task joint covariate selection model, which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Extensive experiments are conducted in order to validate the proposed framework on the SiMES dataset. From the Area Under Curve (AUC) results in multiple ocular diseases classification, our method is shown to outperform the state-of-the-art algorithms.

Original language	English
Title of host publication	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5260-5263
Number of pages	4
ISBN (Electronic)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7319578
Publication status	Published - 4 Nov 2015
Externally published	Yes
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: 25 Aug 2015 → 29 Aug 2015

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2015-November
ISSN (Print)	1557-170X

Conference

Conference	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/Territory	Italy
City	Milan
Period	25/08/15 → 29/08/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2015.7319578

Cite this

Chen, X., Xu, Y., Yin, F., Zhang, Z., Wong, D. W. K., Wong, T. Y., & Liu, J. (2015). Multiple ocular diseases detection based on joint sparse multi-task learning. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (pp. 5260-5263). Article 7319578 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7319578

Chen, Xiangyu ; Xu, Yanwu ; Yin, Fengshou et al. / Multiple ocular diseases detection based on joint sparse multi-task learning. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 5260-5263 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{580e84894f7a457c967783497516216f,

title = "Multiple ocular diseases detection based on joint sparse multi-task learning",

abstract = "In this paper, we present a multiple ocular diseases detection scheme based on joint sparse multi-task learning. Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three major causes of vision impairment and blindness worldwide. The proposed joint sparse multitask learning framework aims to reconstruct a test fundus image with multiple features from as few training subjects as possible. The linear version of this problem could be casted into a multi-task joint covariate selection model, which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Extensive experiments are conducted in order to validate the proposed framework on the SiMES dataset. From the Area Under Curve (AUC) results in multiple ocular diseases classification, our method is shown to outperform the state-of-the-art algorithms.",

author = "Xiangyu Chen and Yanwu Xu and Fengshou Yin and Zhuo Zhang and Wong, \{Damon Wing Kee\} and Wong, \{Tien Yin\} and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 ; Conference date: 25-08-2015 Through 29-08-2015",

year = "2015",

month = nov,

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doi = "10.1109/EMBC.2015.7319578",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Chen, X, Xu, Y, Yin, F, Zhang, Z, Wong, DWK, Wong, TY & Liu, J 2015, Multiple ocular diseases detection based on joint sparse multi-task learning. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015., 7319578, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 5260-5263, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 25/08/15. https://doi.org/10.1109/EMBC.2015.7319578

Multiple ocular diseases detection based on joint sparse multi-task learning. / Chen, Xiangyu; Xu, Yanwu; Yin, Fengshou et al.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5260-5263 7319578 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multiple ocular diseases detection based on joint sparse multi-task learning

AU - Chen, Xiangyu

AU - Xu, Yanwu

AU - Yin, Fengshou

AU - Zhang, Zhuo

AU - Wong, Damon Wing Kee

AU - Wong, Tien Yin

AU - Liu, Jiang

PY - 2015/11/4

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N2 - In this paper, we present a multiple ocular diseases detection scheme based on joint sparse multi-task learning. Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three major causes of vision impairment and blindness worldwide. The proposed joint sparse multitask learning framework aims to reconstruct a test fundus image with multiple features from as few training subjects as possible. The linear version of this problem could be casted into a multi-task joint covariate selection model, which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Extensive experiments are conducted in order to validate the proposed framework on the SiMES dataset. From the Area Under Curve (AUC) results in multiple ocular diseases classification, our method is shown to outperform the state-of-the-art algorithms.

AB - In this paper, we present a multiple ocular diseases detection scheme based on joint sparse multi-task learning. Glaucoma, Pathological Myopia (PM), and Age-related Macular Degeneration (AMD) are three major causes of vision impairment and blindness worldwide. The proposed joint sparse multitask learning framework aims to reconstruct a test fundus image with multiple features from as few training subjects as possible. The linear version of this problem could be casted into a multi-task joint covariate selection model, which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Extensive experiments are conducted in order to validate the proposed framework on the SiMES dataset. From the Area Under Curve (AUC) results in multiple ocular diseases classification, our method is shown to outperform the state-of-the-art algorithms.

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ER -

Chen X, Xu Y, Yin F, Zhang Z, Wong DWK, Wong TY et al. Multiple ocular diseases detection based on joint sparse multi-task learning. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 5260-5263. 7319578. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2015.7319578

Multiple ocular diseases detection based on joint sparse multi-task learning

Abstract

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Conference

UN SDGs

ASJC Scopus subject areas

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