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Many anomaly detection approaches, especially deep learning methods, havebeen recently developed to identify abnormal image morphology by only employingnormal images during training. Unfortunately, many prior anomaly detectionmethods were optimized for a specific "known" abnormality (e.g., brain tumor,bone fraction, cell types). Moreover, even though only the normal images wereused in the training process, the abnormal images were often employed duringthe validation process (e.g., epoch selection, hyper-parameter tuning), whichmight leak the supposed ``unknown" abnormality unintentionally. In this study,we investigated these two essential aspects regarding universal anomalydetection in medical images by (1) comparing various anomaly detection methodsacross four medical datasets, (2) investigating the inevitable but oftenneglected issues on how to unbiasedly select the optimal anomaly detectionmodel during the validation phase using only normal images, and (3) proposing asimple decision-level ensemble method to leverage the advantage of differentkinds of anomaly detection without knowing the abnormality. The results of ourexperiments indicate that none of the evaluated methods consistently achievedthe best performance across all datasets. Our proposed method enhanced therobustness of performance in general (average AUC 0.956).

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images