Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge
This work addresses robustness in mitotic cell detection for histopathology applications, but it is incremental as it builds on existing methods with a new variant.
The paper tackled the problem of detecting mitotic cells in histopathology data with domain shifts by using an adapted YOLOv5s model with a new Radial-Prediction Domain Adaption Classifier and stain augmentation, achieving a test set F1-score of 0.6658.
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.