  This paper describes our contribution to the MIDOG 2022 challenge for
detecting mitotic cells. One of the major problems to be addressed in the MIDOG
2022 challenge is the robustness under the natural variance that appears for
real-life data in the histopathology field. To address the problem, we use an
adapted YOLOv5s model for object detection in conjunction with a new Domain
Adaption Classifier (DAC) variant, the Radial-Prediction-DAC, to achieve
robustness under domain shifts. In addition, we increase the variability of the
available training data using stain augmentation in HED color space. Using the
suggested method, we obtain a test set F1-score of 0.6658.

Computer Vision and Pattern Recognition