Learned Pre-processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
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.
KeywordsRetinal image preprocessing Diabetic retinopathy detection Color balancing
This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project CMUP-ERI/TIC/0028/2014.
- 3.Savelli, B., et al.: Illumination correction by dehazing for retinal vessel segmentation, pp. 219–224, June 2017Google Scholar
- 5.Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597 (2015)Google Scholar
- 9.Galdran, A., Bria, A., Alvarez-Gila, A., Vazquez-Corral, J., Bertalmío, M.: On the duality between retinex and image dehazing. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8212–8221, June 2018Google Scholar
- 11.Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2000)Google Scholar
- 13.Tan, R.: Visibility in bad weather from a single image, June 2008Google Scholar