  Detection of visual anomalies refers to the problem of finding patterns in
different imaging data that do not conform to the expected visual appearance
and is a widely studied problem in different domains. Due to the nature of
anomaly occurrences and underlying generating processes, it is hard to
characterize them and obtain labeled data. Obtaining labeled data is especially
difficult in biomedical applications, where only trained domain experts can
provide labels, which often come in large diversity and complexity. Recently
presented approaches for unsupervised detection of visual anomalies approaches
omit the need for labeled data and demonstrate promising results in domains,
where anomalous samples significantly deviate from the normal appearance.
Despite promising results, the performance of such approaches still lags behind
supervised approaches and does not provide a one-fits-all solution. In this
work, we present an image-to-image translation-based framework that
significantly surpasses the performance of existing unsupervised methods and
approaches the performance of supervised methods in a challenging domain of
cancerous region detection in histology imagery.
