  Annotating data, especially in the medical domain, requires expert knowledge
and a lot of effort. This limits the amount and/or usefulness of available
medical data sets for experimentation. Therefore, developing strategies to
increase the number of annotations while lowering the needed domain knowledge
is of interest. A possible strategy is the use of gamification, i.e.
transforming the annotation task into a game. We propose an approach to gamify
the task of annotating lung fluid cells from pathological whole slide images
(WSIs). As the domain is unknown to non-expert annotators, we transform images
of cells to the domain of flower images using a CycleGAN architecture. In this
more assessable domain, non-expert annotators can be (t)asked to annotate
different kinds of flowers in a playful setting. In order to provide a proof of
concept, this work shows that the domain transfer is possible by evaluating an
image classification network trained on real cell images and tested on the cell
images generated by the CycleGAN network (reconstructed cell images) as well as
real cell images. The classification network reaches an average accuracy of
94.73 % on the original lung fluid cells and 95.25 % on the transformed lung
fluid cells, respectively. Our study lays the foundation for future research on
gamification using CycleGANs.
