Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images

Breast cancer is the most prevalent cancer worldwide and over two million new cases are diagnosed each year. As part of the tumour grading process, histopathologists manually count how many cells are dividing, in a biological process called mitosis. Artificial intelligence (AI) methods have been developed to automatically detect mitotic figures, however these methods often perform poorly when applied to data from outside of the original (training) domain, i.e. they do not generalise well to histology images created using varied staining protocols or digitised using different scanners. Style transfer, a form of domain adaptation, provides the means to transform images from different domains to a shared visual appearance and have been adopted in various applications to mitigate the issue of domain shift. In this paper we train two mitosis detection models and two style transfer methods and evaluate the usefulness of the latter for improving mitosis detection performance in images digitised using different scanners. We found that the best of these models, U-Net without style transfer, achieved an F1-score of 0.693 on the MIDOG 2021 preliminary test set.