  Anomaly detection is the problem of recognizing abnormal inputs based on the
seen examples of normal data. Despite recent advances of deep learning in
recognizing image anomalies, these methods still prove incapable of handling
complex medical images, such as barely visible abnormalities in chest X-rays
and metastases in lymph nodes. To address this problem, we introduce a new
powerful method of image anomaly detection. It relies on the classical
autoencoder approach with a re-designed training pipeline to handle
high-resolution, complex images and a robust way of computing an image
abnormality score. We revisit the very problem statement of fully unsupervised
anomaly detection, where no abnormal examples at all are provided during the
model setup. We propose to relax this unrealistic assumption by using a very
small number of anomalies of confined variability merely to initiate the search
of hyperparameters of the model. We evaluate our solution on natural image
datasets with a known benchmark, as well as on two medical datasets containing
radiology and digital pathology images. The proposed approach suggests a new
strong baseline for image anomaly detection and outperforms state-of-the-art
approaches in complex medical image analysis tasks.
