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In histopathology, scanner-induced domain shifts are known to impede theperformance of trained neural networks when tested on unseen data. Multi-domainpre-training or dedicated domain-generalization techniques can help to developdomain-agnostic algorithms. For this, multi-scanner datasets with a highvariety of slide scanning systems are highly desirable. We present a publiclyavailable multi-scanner dataset of canine cutaneous squamous cell carcinomahistopathology images, composed of 44 samples digitized with five slidescanners. This dataset provides local correspondences between images andthereby isolates the scanner-induced domain shift from other inherent, e.g.morphology-induced domain shifts. To highlight scanner differences, we presenta detailed evaluation of color distributions, sharpness, and contrast of theindividual scanner subsets. Additionally, to quantify the inherentscanner-induced domain shift, we train a tumor segmentation network on eachscanner subset and evaluate the performance both in- and cross-domain. Weachieve a class-averaged in-domain intersection over union coefficient of up to0.86 and observe a cross-domain performance decrease of up to 0.38, whichconfirms the inherent domain shift of the presented dataset and its negativeimpact on the performance of deep neural networks.

Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset