Deep semi-supervised and self-supervised learning for diabetic retinopathy detection

Jose Arrieta; Oscar J. Perdomo; Fabio A. González

doi:10.1117/12.2669723

6 March 2023 Deep semi-supervised and self-supervised learning for diabetic retinopathy detection

Jose Arrieta, Oscar J. Perdomo, Fabio A. González

Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 125671J (2023) https://doi.org/10.1117/12.2669723
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile

Abstract

Diabetic retinopathy (DR) is one of the leading causes of blindness in the working-age population of developed countries, caused by a side effect of diabetes that reduces the blood supply to the retina. Deep neural networks have been widely used in automated systems for DR classification on eye fundus images. However, these models need a large number of annotated images. In the medical domain, annotations from experts are costly, tedious, and time-consuming; as a result, a limited number of annotated images are available. This paper presents a semi-supervised method that leverages unlabeled images and labeled ones to train a model that detects diabetic retinopathy. The proposed method uses unsupervised pretraining via self-supervised learning followed by supervised fine-tuning with a small set of labeled images and knowledge distillation to increase the performance in a classification task. This method was evaluated on the EyePACS test and Messidor-2 dataset achieving 0.94 and 0.89 AUC respectively using only 2% of EyePACS train labeled images.

Citation Download Citation

Jose Arrieta, Oscar J. Perdomo, and Fabio A. González "Deep semi-supervised and self-supervised learning for diabetic retinopathy detection", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 125671J (6 March 2023); https://doi.org/10.1117/12.2669723

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