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