  This work addresses the detection of Helicobacter pylori a bacterium
classified since 1994 as class 1 carcinogen to humans. By its highest
specificity and sensitivity, the preferred diagnosis technique is the analysis
of histological images with immunohistochemical staining, a process in which
certain stained antibodies bind to antigens of the biological element of
interest. This analysis is a time demanding task, which is currently done by an
expert pathologist that visually inspects the digitized samples.
  We propose to use autoencoders to learn latent patterns of healthy tissue and
detect H. pylori as an anomaly in image staining. Unlike existing
classification approaches, an autoencoder is able to learn patterns in an
unsupervised manner (without the need of image annotations) with high
performance. In particular, our model has an overall 91% of accuracy with 86\%
sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.
