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

Authors: Smailagic, A., Sharan, A., Costa, P., Galdran, A., Gaudio, A. and Campilho, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 11663 LNCS

Pages: 362-368

eISSN: 1611-3349

ISBN: 9783030272715

ISSN: 0302-9743

DOI: 10.1007/978-3-030-27272-2_32

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.

Source: Scopus

Learned Pre-processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images

Authors: Smailagic, A., Sharan, A., Costa, P., Galdran, A., Gaudio, A. and Campilho, A.

Journal: IMAGE ANALYSIS AND RECOGNITION (ICIAR 2019), PT II

Volume: 11663

Pages: 362-368

eISSN: 1611-3349

ISBN: 978-3-030-27271-5

ISSN: 0302-9743

DOI: 10.1007/978-3-030-27272-2_32

Source: Web of Science (Lite)

Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images.

Authors: Smailagic, A., Sharan, A., Costa, P., Galdran, A., Gaudio, A. and Campilho, A.

Journal: CoRR

Volume: abs/2007.13838

Source: DBLP

Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images

Authors: Smailagic, A., Sharan, A., Costa, P., Galdran, A., Gaudio, A. and Campilho, A.

DOI: 10.1007/978-3-030-27272-2_32

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.

http://dx.doi.org/10.1007/978-3-030-27272-2_32

Source: arXiv