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Due to the high cost of manually annotating medical images, especially forlarge-scale datasets, anomaly detection has been explored through trainingmodels with only normal data. Lacking prior knowledge of true anomalies is themain reason for the limited application of previous anomaly detection methods,especially in the medical image analysis realm. In this work, we propose aone-shot anomaly detection framework, namely LesionPaste, that utilizes trueanomalies from a single annotated sample and synthesizes artificial anomaloussamples for anomaly detection. First, a lesion bank is constructed by applyingaugmentation to randomly selected lesion patches. Then, MixUp is adopted topaste patches from the lesion bank at random positions in normal images tosynthesize anomalous samples for training. Finally, a classification network istrained using the synthetic abnormal samples and the true normal data.Extensive experiments are conducted on two publicly-available medical imagedatasets with different types of abnormalities. On both datasets, our proposedLesionPaste largely outperforms several state-of-the-art unsupervised andsemi-supervised anomaly detection methods, and is on a par with thefully-supervised counterpart. To note, LesionPaste is even better than thefully-supervised method in detecting early-stage diabetic retinopathy.

LesionPaste: One-Shot Anomaly Detection for Medical Images