Learned pre-processing for automatic diabetic retinopathy detection on eye fundus images

  • Asim Smailagic
  • , Anupma Sharan
  • , Pedro Costa*
  • , Adrian Galdran
  • , Alex Gaudio
  • , Aurélio Campilho
  • *Corresponding author for this work

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

7 Citations (Scopus)

Abstract

Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world. The main aim of this paper is to improve the accuracy of Diabetic Retinopathy detection by implementing a shadow removal and color correction step as a preprocessing stage from eye fundus images. For this, we rely on recent findings indicating that application of image dehazing on the inverted intensity domain amounts to illumination compensation. Inspired by this work, we propose a Shadow Removal Layer that allows us to learn the pre-processing function for a particular task. We show that learning the pre-processing function improves the performance of the network on the Diabetic Retinopathy detection task.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
EditorsFakhri Karray, Alfred Yu, Aurélio Campilho
PublisherSpringer Verlag
Pages362-368
Number of pages7
ISBN (Print)9783030272715
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event16th International Conference on Image Analysis and Recognition, ICIAR 2019 - Waterloo, Canada
Duration: 27 Aug 201929 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11663 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Image Analysis and Recognition, ICIAR 2019
Country/TerritoryCanada
CityWaterloo
Period27/08/1929/08/19

Keywords

  • Color balancing
  • Diabetic retinopathy detection
  • Retinal image preprocessing

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