  Domain variability is a common bottle neck in developing generalisable
algorithms for various medical applications. Motivated by the observation that
the domain variability of the medical images is to some extent compact, we
propose to learn a target representative feature space through unpaired image
to image translation (CycleGAN). We comprehensively evaluate the performanceand
usefulness by utilising the transformation to mitosis detection with candidate
proposal and classification. This work presents a simple yet effective
multi-step mitotic figure detection algorithm developed as a baseline for the
MIDOG challenge. On the preliminary test set, the algorithm scoresan F1 score
of 0.52.
