Radial Prediction Domain Adaption Classifier for the MIDOG 2022 challenge

In this paper, we describe our contribution to the MIDOG 2022 challenge without using additional data. A challenge to handle the distribution shift between different tissues for detection of mitosis cells. The main characteristics parts can be distinguished into three parts: We modify the Radial Prediction Layer (RPL) to integrate the layer in a domain adaption classifier, the Prediction Domain Adaption Classifier (RP-DAC). This developed variant learns prototypes for each class and brings more related classes closer. We used this to learn the scanner, the tissue, and the case id. We used multiple trained YOLO models with different modified input variants of the image. We combine the outputs of the model with an ensembling strategy. We use the HED color space for data augmentation by calculating different magnitudes for each scanner/tissue type to create more variance in the training set.