Fundus Image Quality-Guided Diabetic Retinopathy Grading

Kang Zhou; Zaiwang Gu; Annan Li; Jun Cheng; Shenghua Gao; Jiang Liu

doi:10.1007/978-3-030-00949-6_29

Fundus Image Quality-Guided Diabetic Retinopathy Grading

Kang Zhou^*
, Zaiwang Gu
, Annan Li
, Jun Cheng
, Shenghua Gao
, Jiang Liu

^*Corresponding author for this work

School of Computer Science and Engineering

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

9 Scopus citations

Abstract

With the increasing use of fundus cameras, we can get a large number of retinal images. However there are quite a number of images in poor quality because of uneven illumination, occlusion and so on. The quality of images significantly affects the performance of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that did not face the unbalanced distribution, we propose weighted softmax with center loss to solve the unbalanced data distribution in medical images. Furthermore, we propose Fundus Image Quality (FIQ)-guided DR grading method based on multi-task deep learning, which is the first work using fundus image quality to help grade DR. Experimental results on the Kaggle dataset show that fundus image quality greatly impact DR grading. By considering the influence of quality, the experimental results validate the effectiveness of our propose method. All codes and fundus image quality label on Kaggle DR dataset are released in https://github.com/ClancyZhou/kaggle_DR_image_quality_miccai2018_workshop.

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	245-252
Number of pages	8
ISBN (Print)	9783030009489
DOIs	https://doi.org/10.1007/978-3-030-00949-6_29
State	Published - 2018
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 Sep 2018 → 20 Sep 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

Keywords

DR screening
Deep learning
Fundus image quality classification
Multi-task

Access to Document

10.1007/978-3-030-00949-6_29

Cite this

Zhou, K., Gu, Z., Li, A., Cheng, J., Gao, S., & Liu, J. (2018). Fundus Image Quality-Guided Diabetic Retinopathy Grading. 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. 245-252). (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_29

Zhou, Kang ; Gu, Zaiwang ; Li, Annan et al. / Fundus Image Quality-Guided Diabetic Retinopathy Grading. 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. 245-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a1cf4cc6c3944d20b30c3fe8e20f6cb3,

title = "Fundus Image Quality-Guided Diabetic Retinopathy Grading",

abstract = "With the increasing use of fundus cameras, we can get a large number of retinal images. However there are quite a number of images in poor quality because of uneven illumination, occlusion and so on. The quality of images significantly affects the performance of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that did not face the unbalanced distribution, we propose weighted softmax with center loss to solve the unbalanced data distribution in medical images. Furthermore, we propose Fundus Image Quality (FIQ)-guided DR grading method based on multi-task deep learning, which is the first work using fundus image quality to help grade DR. Experimental results on the Kaggle dataset show that fundus image quality greatly impact DR grading. By considering the influence of quality, the experimental results validate the effectiveness of our propose method. All codes and fundus image quality label on Kaggle DR dataset are released in https://github.com/ClancyZhou/kaggle\_DR\_image\_quality\_miccai2018\_workshop.",

keywords = "DR screening, Deep learning, Fundus image quality classification, Multi-task",

author = "Kang Zhou and Zaiwang Gu and Annan Li and Jun Cheng and Shenghua Gao 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\_29",

language = "英语",

isbn = "9783030009489",

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

publisher = "Springer Verlag",

pages = "245--252",

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 = "德国",

}

Zhou, K, Gu, Z, Li, A, Cheng, J, Gao, S & Liu, J 2018, Fundus Image Quality-Guided Diabetic Retinopathy Grading. 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. 245-252, 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_29

Fundus Image Quality-Guided Diabetic Retinopathy Grading. / Zhou, Kang; Gu, Zaiwang; Li, Annan 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. 245-252 (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/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fundus Image Quality-Guided Diabetic Retinopathy Grading

AU - Zhou, Kang

AU - Gu, Zaiwang

AU - Li, Annan

AU - Cheng, Jun

AU - Gao, Shenghua

AU - Liu, Jiang

PY - 2018

Y1 - 2018

N2 - With the increasing use of fundus cameras, we can get a large number of retinal images. However there are quite a number of images in poor quality because of uneven illumination, occlusion and so on. The quality of images significantly affects the performance of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that did not face the unbalanced distribution, we propose weighted softmax with center loss to solve the unbalanced data distribution in medical images. Furthermore, we propose Fundus Image Quality (FIQ)-guided DR grading method based on multi-task deep learning, which is the first work using fundus image quality to help grade DR. Experimental results on the Kaggle dataset show that fundus image quality greatly impact DR grading. By considering the influence of quality, the experimental results validate the effectiveness of our propose method. All codes and fundus image quality label on Kaggle DR dataset are released in https://github.com/ClancyZhou/kaggle_DR_image_quality_miccai2018_workshop.

AB - With the increasing use of fundus cameras, we can get a large number of retinal images. However there are quite a number of images in poor quality because of uneven illumination, occlusion and so on. The quality of images significantly affects the performance of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that did not face the unbalanced distribution, we propose weighted softmax with center loss to solve the unbalanced data distribution in medical images. Furthermore, we propose Fundus Image Quality (FIQ)-guided DR grading method based on multi-task deep learning, which is the first work using fundus image quality to help grade DR. Experimental results on the Kaggle dataset show that fundus image quality greatly impact DR grading. By considering the influence of quality, the experimental results validate the effectiveness of our propose method. All codes and fundus image quality label on Kaggle DR dataset are released in https://github.com/ClancyZhou/kaggle_DR_image_quality_miccai2018_workshop.

KW - DR screening

KW - Deep learning

KW - Fundus image quality classification

KW - Multi-task

UR - https://www.scopus.com/pages/publications/85053922387

U2 - 10.1007/978-3-030-00949-6_29

DO - 10.1007/978-3-030-00949-6_29

M3 - 会议稿件

AN - SCOPUS:85053922387

SN - 9783030009489

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

SP - 245

EP - 252

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 -

Zhou K, Gu Z, Li A, Cheng J, Gao S, Liu J. Fundus Image Quality-Guided Diabetic Retinopathy Grading. 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. 245-252. (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_29

Fundus Image Quality-Guided Diabetic Retinopathy Grading

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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