First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain

• URL: http://arxiv.org/abs/2111.03663v1
• Date: Fri, 5 Nov 2021 14:11:38 GMT
• Title: First steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain
• Authors: Sonja Kunzmann, Christian Marzahl, Felix Denzinger, Christof A. Bertram, Robert Klopfleisch, Katharina Breininger, Vincent Christlein, Andreas Maier
• Abstract summary: We propose an approach to gamify the task of annotating lung fluid cells from pathological whole slide images. As this domain is unknown to non-expert annotators, we transform images of cells detected with a RetinaNet architecture to the domain of flower images. In this more assessable domain, non-expert annotators can be (t)asked to annotate different kinds of flowers in a playful setting.
• Score: 6.470549137572311
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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, that is 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. As this domain is unknown to non-expert annotators, we transform images of cells detected with a RetinaNet architecture to the domain of flower images. This domain transfer is performed with a CycleGAN architecture for different cell types. 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. The classification network reaches an accuracy of 97.48% and 95.16% on the original lung fluid cells and transformed lung fluid cells, respectively. With this study, we lay the foundation for future research on gamification using CycleGANs.

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