  Oversight in medical images is a crucial problem, and timely reporting of
medical images is desired. Therefore, an all-purpose anomaly detection method
that can detect virtually all types of lesions/diseases in a given image is
strongly desired. However, few commercially available and versatile anomaly
detection methods for medical images have been provided so far. Recently,
anomaly detection methods built upon deep learning methods have been rapidly
growing in popularity, and these methods seem to provide reasonable solutions
to the problem. However, the workload to label the images necessary for
training in deep learning remains heavy. In this study, we present an anomaly
detection method based on two trained flow-based generative models. With this
method, the posterior probability can be computed as a normality metric for any
given image. The training of the generative models requires two sets of images:
a set containing only normal images and another set containing both normal and
abnormal images without any labels. In the latter set, each sample does not
have to be labeled as normal or abnormal; therefore, any mixture of images
(e.g., all cases in a hospital) can be used as the dataset without cumbersome
manual labeling. The method was validated with two types of medical images:
chest X-ray radiographs (CXRs) and brain computed tomographies (BCTs). The
areas under the receiver operating characteristic curves for logarithm
posterior probabilities of CXRs (0.868 for pneumonia-like opacities) and BCTs
(0.904 for infarction) were comparable to those in previous studies with other
anomaly detection methods. This result showed the versatility of our method.
