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

arXiv: https://arxiv.org/abs/2107.09372 · HTML: https://mlsanity.com/paper/2107.09372/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [Self-supervised Domain Adaptation for Computer Vision Tasks](https://mlsanity.com/paper/1907.10915/) (2019)
- [JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation](https://mlsanity.com/paper/2004.07054/) (2020)
- [A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability](https://mlsanity.com/paper/2008.09772/) (2020)
- [Robust Retinal Vessel Segmentation from a Data Augmentation Perspective](https://mlsanity.com/paper/2007.15883/) (2020)
- [Attentive Normalization for Conditional Image Generation](https://mlsanity.com/paper/2004.03828/) (2020)
- [TATL: Task Agnostic Transfer Learning for Skin Attributes Detection](https://mlsanity.com/paper/2104.01641/) (2021)
- [The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report](https://mlsanity.com/paper/2005.09448/) (2020)
- [Self-Supervised Domain Adaptation with Consistency Training](https://mlsanity.com/paper/2010.07539/) (2020)

## Built on by (most cited first, 5 of 5)

- [Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering](https://mlsanity.com/paper/2212.01893/) (2022)
- [Prompt-driven Latent Domain Generalization for Medical Image Classification](https://mlsanity.com/paper/2401.03002/) (2024)
- [DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading](https://mlsanity.com/paper/2212.14615/) (2022)
- [Combining datasets to increase the number of samples and improve model fitting](https://mlsanity.com/paper/2210.05165/) (2022)
- [Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data](https://mlsanity.com/paper/2412.01203/) (2024)

## 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.