  For histopathological tumor assessment, the count of mitotic figures per area
is an important part of prognostication. Algorithmic approaches - such as for
mitotic figure identification - have significantly improved in recent times,
potentially allowing for computer-augmented or fully automatic screening
systems in the future. This trend is further supported by whole slide scanning
microscopes becoming available in many pathology labs and could soon become a
standard imaging tool.
  For an application in broader fields of such algorithms, the availability of
mitotic figure data sets of sufficient size for the respective tissue type and
species is an important precondition, that is, however, rarely met. While
algorithmic performance climbed steadily for e.g. human mammary carcinoma,
thanks to several challenges held in the field, for most tumor types, data sets
are not available.
  In this work, we assess domain transfer of mitotic figure recognition using
domain adversarial training on four data sets, two from dogs and two from
humans. We were able to show that domain adversarial training considerably
improves accuracy when applying mitotic figure classification learned from the
canine on the human data sets (up to +12.8% in accuracy) and is thus a helpful
method to transfer knowledge from existing data sets to new tissue types and
species.
