  Computer-aided systems in histopathology are often challenged by various
sources of domain shift that impact the performance of these algorithms
considerably. We investigated the potential of using self-supervised
pre-training to overcome scanner-induced domain shifts for the downstream task
of tumor segmentation. For this, we present the Barlow Triplets to learn
scanner-invariant representations from a multi-scanner dataset with local image
correspondences. We show that self-supervised pre-training successfully aligned
different scanner representations, which, interestingly only results in a
limited benefit for our downstream task. We thereby provide insights into the
influence of scanner characteristics for downstream applications and contribute
to a better understanding of why established self-supervised methods have not
yet shown the same success on histopathology data as they have for natural
images.
