AugPaste: A one-shot approach for diabetic retinopathy detection

Unsupervised anomaly detection methods aim to reduce the cost of manually annotating abnormal medical image datasets. However, since they are not trained on massive abnormal images, their discriminative capability may be low. In this paper, we present AugPaste, a novel one-shot anomaly detection framework for detecting diabetic retinopathy (DR) from fundus images. AugPaste utilizes true anomalies from a single annotated DR sample to synthesize a large amount of artificial DR fundus images. The framework begins with constructing a DR lesion bank through augmentation of randomly selected DR lesion patches. Synthesized DR samples are then generated by pasting lesion patches selected from the lesion bank into normal images using various prior knowledge -guided strategies. We finally train a classification network on the synthetic abnormal images along with true normal images for anomaly detection. Our tests on four public fundus image datasets show that AugPaste outperforms leading unsupervised and few -shot methods and rivals fully -supervised methods. The source code is available at https://github.com/Aidanvk/AugPaste.