  In histopathology, scanner-induced domain shifts are known to impede the
performance of trained neural networks when tested on unseen data. Multi-domain
pre-training or dedicated domain-generalization techniques can help to develop
domain-agnostic algorithms. For this, multi-scanner datasets with a high
variety of slide scanning systems are highly desirable. We present a publicly
available multi-scanner dataset of canine cutaneous squamous cell carcinoma
histopathology images, composed of 44 samples digitized with five slide
scanners. This dataset provides local correspondences between images and
thereby isolates the scanner-induced domain shift from other inherent, e.g.
morphology-induced domain shifts. To highlight scanner differences, we present
a detailed evaluation of color distributions, sharpness, and contrast of the
individual scanner subsets. Additionally, to quantify the inherent
scanner-induced domain shift, we train a tumor segmentation network on each
scanner subset and evaluate the performance both in- and cross-domain. We
achieve a class-averaged in-domain intersection over union coefficient of up to
0.86 and observe a cross-domain performance decrease of up to 0.38, which
confirms the inherent domain shift of the presented dataset and its negative
impact on the performance of deep neural networks.
