First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain
This addresses the challenge of annotation scarcity in medical imaging for researchers, though it is incremental as it builds on existing CycleGAN methods.
The paper tackles the problem of limited expert annotations for medical data by proposing a gamification approach for annotating lung fluid cells, using a CycleGAN to transform cell images into flower images for non-experts, achieving classification accuracies of 94.73% on original cells and 95.25% on transformed cells.
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.