Fundus Image Quality-Guided Diabetic Retinopathy Grading

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

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

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 languageEnglish
Title of host publicationComputational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsZeike 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
PublisherSpringer Verlag
Pages245-252
Number of pages8
ISBN (Print)9783030009489
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event1st 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 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11039 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st 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/TerritorySpain
CityGranada
Period16/09/1820/09/18

Keywords

  • Deep learning
  • DR screening
  • Fundus image quality classification
  • Multi-task

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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