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This work addresses the detection of Helicobacter pylori a bacteriumclassified since 1994 as class 1 carcinogen to humans. By its highestspecificity and sensitivity, the preferred diagnosis technique is the analysisof histological images with immunohistochemical staining, a process in whichcertain stained antibodies bind to antigens of the biological element ofinterest. This analysis is a time demanding task, which is currently done by anexpert pathologist that visually inspects the digitized samples.  We propose to use autoencoders to learn latent patterns of healthy tissue anddetect H. pylori as an anomaly in image staining. Unlike existingclassification approaches, an autoencoder is able to learn patterns in anunsupervised manner (without the need of image annotations) with highperformance. 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.

Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images