Large-Scale Left and Right Eye Classification in Retinal Images

Peng Liu; Zaiwang Gu; Fan Liu; Yuming Jiang; Shanshan Jiang; Haoyu Mao; Jun Cheng; Lixin Duan; Jiang Liu

doi:10.1007/978-3-030-00949-6_31

Large-Scale Left and Right Eye Classification in Retinal Images

Peng Liu
, Zaiwang Gu
, Fan Liu
, Yuming Jiang
, Shanshan Jiang
, Haoyu Mao
, Jun Cheng
, Lixin Duan
, Jiang Liu

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

5 Citations (Scopus)

Abstract

Left and right eye information is an important priori for automatic retinal fundus image analysis. However, such information is often not available or even wrongly provided in many datasets. In this work, we spend a considerable amount of efforts in manually annotating the left and right eyes from the large-scale Kaggle Diabetic Retinopathy dataset consisting of 88,702 fundus images, based on our developed online labeling system. With the newly annotated large-scale dataset, we also train classification models based on convolutional neural networks to discriminate left and right eyes in fundus images. As experimentally evaluated on the Kaggle and Origa dataset, our trained deep learning models achieve 99.90% and 99.23% in term of classification accuracy, respectively, which can be considered for practical use.

Original language	English
Title of host publication	Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
Editors	Zeike Taylor, Hrvoje Bogunovic, David Snead, Mona K. Garvin, Xin Jan Chen, Francesco Ciompi, Yanwu Xu, Lena Maier-Hein, Mitko Veta, Emanuele Trucco, Danail Stoyanov, Nasir Rajpoot, Jeroen van der Laak, Anne Martel, Stephen McKenna
Publisher	Springer Verlag
Pages	261-268
Number of pages	8
ISBN (Print)	9783030009489
DOIs	https://doi.org/10.1007/978-3-030-00949-6_31
Publication status	Published - 2018
Externally published	Yes
Event	1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018 - Granada, Spain Duration: 16 Sept 2018 → 20 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11039 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 20/09/18

UN SDGs

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

SDG 3 Good Health and Well-being
SDG 4 Quality Education

Free Keywords

Convolutional neural networks
Left and right eye classification

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-00949-6_31

Cite this

Liu, P., Gu, Z., Liu, F., Jiang, Y., Jiang, S., Mao, H., Cheng, J., Duan, L., & Liu, J. (2018). Large-Scale Left and Right Eye Classification in Retinal Images. In Z. Taylor, H. Bogunovic, D. Snead, M. K. Garvin, X. J. Chen, F. Ciompi, Y. Xu, L. Maier-Hein, M. Veta, E. Trucco, D. Stoyanov, N. Rajpoot, J. van der Laak, A. Martel, & S. McKenna (Eds.), Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 261-268). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11039 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00949-6_31

Liu, Peng ; Gu, Zaiwang ; Liu, Fan et al. / Large-Scale Left and Right Eye Classification in Retinal Images. Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Zeike Taylor ; Hrvoje Bogunovic ; David Snead ; Mona K. Garvin ; Xin Jan Chen ; Francesco Ciompi ; Yanwu Xu ; Lena Maier-Hein ; Mitko Veta ; Emanuele Trucco ; Danail Stoyanov ; Nasir Rajpoot ; Jeroen van der Laak ; Anne Martel ; Stephen McKenna. Springer Verlag, 2018. pp. 261-268 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{bbea3519611e409b9d01eb2609f954f5,

title = "Large-Scale Left and Right Eye Classification in Retinal Images",

abstract = "Left and right eye information is an important priori for automatic retinal fundus image analysis. However, such information is often not available or even wrongly provided in many datasets. In this work, we spend a considerable amount of efforts in manually annotating the left and right eyes from the large-scale Kaggle Diabetic Retinopathy dataset consisting of 88,702 fundus images, based on our developed online labeling system. With the newly annotated large-scale dataset, we also train classification models based on convolutional neural networks to discriminate left and right eyes in fundus images. As experimentally evaluated on the Kaggle and Origa dataset, our trained deep learning models achieve 99.90\% and 99.23\% in term of classification accuracy, respectively, which can be considered for practical use.",

keywords = "Convolutional neural networks, Left and right eye classification",

author = "Peng Liu and Zaiwang Gu and Fan Liu and Yuming Jiang and Shanshan Jiang and Haoyu Mao and Jun Cheng and Lixin Duan and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018 ; Conference date: 16-09-2018 Through 20-09-2018",

year = "2018",

doi = "10.1007/978-3-030-00949-6\_31",

language = "English",

isbn = "9783030009489",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "261--268",

editor = "Zeike Taylor and Hrvoje Bogunovic and David Snead and Garvin, \{Mona K.\} and Chen, \{Xin Jan\} and Francesco Ciompi and Yanwu Xu and Lena Maier-Hein and Mitko Veta and Emanuele Trucco and Danail Stoyanov and Nasir Rajpoot and \{van der Laak\}, Jeroen and Anne Martel and Stephen McKenna",

booktitle = "Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings",

address = "Germany",

}

Liu, P, Gu, Z, Liu, F, Jiang, Y, Jiang, S, Mao, H, Cheng, J, Duan, L & Liu, J 2018, Large-Scale Left and Right Eye Classification in Retinal Images. in Z Taylor, H Bogunovic, D Snead, MK Garvin, XJ Chen, F Ciompi, Y Xu, L Maier-Hein, M Veta, E Trucco, D Stoyanov, N Rajpoot, J van der Laak, A Martel & S McKenna (eds), Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11039 LNCS, Springer Verlag, pp. 261-268, 1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00949-6_31

Large-Scale Left and Right Eye Classification in Retinal Images. / Liu, Peng; Gu, Zaiwang; Liu, Fan et al.
Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Zeike Taylor; Hrvoje Bogunovic; David Snead; Mona K. Garvin; Xin Jan Chen; Francesco Ciompi; Yanwu Xu; Lena Maier-Hein; Mitko Veta; Emanuele Trucco; Danail Stoyanov; Nasir Rajpoot; Jeroen van der Laak; Anne Martel; Stephen McKenna. Springer Verlag, 2018. p. 261-268 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11039 LNCS).

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

TY - GEN

T1 - Large-Scale Left and Right Eye Classification in Retinal Images

AU - Liu, Peng

AU - Gu, Zaiwang

AU - Liu, Fan

AU - Jiang, Yuming

AU - Jiang, Shanshan

AU - Mao, Haoyu

AU - Cheng, Jun

AU - Duan, Lixin

AU - Liu, Jiang

PY - 2018

Y1 - 2018

N2 - Left and right eye information is an important priori for automatic retinal fundus image analysis. However, such information is often not available or even wrongly provided in many datasets. In this work, we spend a considerable amount of efforts in manually annotating the left and right eyes from the large-scale Kaggle Diabetic Retinopathy dataset consisting of 88,702 fundus images, based on our developed online labeling system. With the newly annotated large-scale dataset, we also train classification models based on convolutional neural networks to discriminate left and right eyes in fundus images. As experimentally evaluated on the Kaggle and Origa dataset, our trained deep learning models achieve 99.90% and 99.23% in term of classification accuracy, respectively, which can be considered for practical use.

AB - Left and right eye information is an important priori for automatic retinal fundus image analysis. However, such information is often not available or even wrongly provided in many datasets. In this work, we spend a considerable amount of efforts in manually annotating the left and right eyes from the large-scale Kaggle Diabetic Retinopathy dataset consisting of 88,702 fundus images, based on our developed online labeling system. With the newly annotated large-scale dataset, we also train classification models based on convolutional neural networks to discriminate left and right eyes in fundus images. As experimentally evaluated on the Kaggle and Origa dataset, our trained deep learning models achieve 99.90% and 99.23% in term of classification accuracy, respectively, which can be considered for practical use.

KW - Convolutional neural networks

KW - Left and right eye classification

UR - https://www.scopus.com/pages/publications/85053887032

U2 - 10.1007/978-3-030-00949-6_31

DO - 10.1007/978-3-030-00949-6_31

M3 - Conference contribution

AN - SCOPUS:85053887032

SN - 9783030009489

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 261

EP - 268

BT - Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings

A2 - Taylor, Zeike

A2 - Bogunovic, Hrvoje

A2 - Snead, David

A2 - Garvin, Mona K.

A2 - Chen, Xin Jan

A2 - Ciompi, Francesco

A2 - Xu, Yanwu

A2 - Maier-Hein, Lena

A2 - Veta, Mitko

A2 - Trucco, Emanuele

A2 - Stoyanov, Danail

A2 - Rajpoot, Nasir

A2 - van der Laak, Jeroen

A2 - Martel, Anne

A2 - McKenna, Stephen

PB - Springer Verlag

T2 - 1st International Workshop on Computational Pathology, COMPAY 2018 and 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018 Held in Conjunction with MICCAI 2018

Y2 - 16 September 2018 through 20 September 2018

ER -

Liu P, Gu Z, Liu F, Jiang Y, Jiang S, Mao H et al. Large-Scale Left and Right Eye Classification in Retinal Images. In Taylor Z, Bogunovic H, Snead D, Garvin MK, Chen XJ, Ciompi F, Xu Y, Maier-Hein L, Veta M, Trucco E, Stoyanov D, Rajpoot N, van der Laak J, Martel A, McKenna S, editors, Computational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer Verlag. 2018. p. 261-268. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00949-6_31

Large-Scale Left and Right Eye Classification in Retinal Images

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

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