AugPaste: A one-shot approach for diabetic retinopathy detection

Qiu，Jiaming1; Huang，Weikai1; Huang，Yijin1,2; Yu，Nanxi1; Tang，Xiaoying1,3

2024-10-01

10.1016/j.bspc.2024.106489

Biomedical Signal Processing and Control   影响因子和分区

1746-8094

1746-8108

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.

Anomaly detection Anomaly synthesis Diabetic retinopathy Fundus image One-shot learning

SCI ; EI

英语

第一 ; 通讯

20242216187973

Eye protection ; Medical imaging

Biomedical Engineering:461.1 ; Imaging Techniques:746 ; Accidents and Accident Prevention:914.1

2-s2.0-85194493626

Scopus

1.Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.School of Biomedical Engineering,The University of British Columbia,Vancouver,BC V6T 1Z3,Canada
3.Jiaxing Research Institute,Southern University of Science and Technology,Jiaxing,314001,China

Qiu，Jiaming,Huang，Weikai,Huang，Yijin,et al. AugPaste: A one-shot approach for diabetic retinopathy detection[J]. Biomedical Signal Processing and Control,2024,96.

Qiu，Jiaming,Huang，Weikai,Huang，Yijin,Yu，Nanxi,&Tang，Xiaoying.(2024).AugPaste: A one-shot approach for diabetic retinopathy detection.Biomedical Signal Processing and Control,96.

Qiu，Jiaming,et al."AugPaste: A one-shot approach for diabetic retinopathy detection".Biomedical Signal Processing and Control 96(2024).