LesionPaste: One-Shot Anomaly Detection for Medical Images

TL;DR

LesionPaste is a novel one-shot anomaly detection framework for medical images that synthesizes artificial anomalies from a single annotated sample, significantly improving detection performance over existing methods.

Contribution

It introduces a new one-shot learning approach using lesion augmentation and MixUp to generate synthetic anomalies for training, advancing medical image anomaly detection.

Findings

Outperforms state-of-the-art unsupervised and semi-supervised methods.

Achieves performance comparable to fully-supervised approaches.

Excels in early-stage diabetic retinopathy detection.

Abstract

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.