  Automated detection of mitotic figures in histopathology images has seen vast
improvements, thanks to modern deep learning-based pipelines. Application of
these methods, however, is in practice limited by strong variability of images
between labs. This results in a domain shift of the images, which causes a
performance drop of the models. Hypothesizing that the scanner device plays a
decisive role in this effect, we evaluated the susceptibility of a standard
mitosis detection approach to the domain shift introduced by using a different
whole slide scanner. Our work is based on the MICCAI-MIDOG challenge 2021 data
set, which includes 200 tumor cases of human breast cancer and four scanners.
  Our work indicates that the domain shift induced not by biochemical
variability but purely by the choice of acquisition device is underestimated so
far. Models trained on images of the same scanner yielded an average F1 score
of 0.683, while models trained on a single other scanner only yielded an
average F1 score of 0.325. Training on another multi-domain mitosis dataset led
to mean F1 scores of 0.52. We found this not to be reflected by domain-shifts
measured as proxy A distance-derived metric.
