mlsanityData to 7 Oct 2026

Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection

arXiv 2208.02408 ↗First posted 4 Aug 2022cs.CVcited by 1

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Abstract (arXiv)

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 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 4 Aug 2022.

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17 of 30 references.

References · 17

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5 more
  1. 2018Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Surveycited by 19
  2. 2021Intermediate Layers Matter in Momentum Contrastive Self Supervised Learningcited by 19
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Citing papers per year: 2025: 1.

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  1. 2025From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screeningcited by 0