mlsanityData to 7 Oct 2026

Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction

arXiv 2107.09372 ↗First posted 20 Jul 2021cs.CVcited by 5

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Abstract (arXiv)

This paper investigates the problem of domain adaptation for diabetic retinopathy (DR) grading. We learn invariant target-domain features by defining a novel self-supervised task based on retinal vessel image reconstructions, inspired by medical domain knowledge. Then, a benchmark of current state-of-the-art unsupervised domain adaptation methods on the DR problem is provided. It can be shown that our approach outperforms existing domain adaption strategies. Furthermore, when utilizing entire training data in the target domain, we are able to compete with several state-of-the-art approaches in final classification accuracy just by applying standard network architectures and using image-level labels.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 24 of its 42 references that are in the map. First posted on arXiv 20 Jul 2021.

Builds on

24 of 42 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 2.

5 papers.

  1. 2022Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clusteringcited by 16
  2. 2024Prompt-driven Latent Domain Generalization for Medical Image Classificationcited by 8
  3. 2022DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Gradingcited by 3
  4. 2022Combining datasets to increase the number of samples and improve model fittingcited by 2
  5. 2024Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Datacited by 1