  Breast cancer is the most commonly diagnosed cancer worldwide, with over two
million new cases each year. During diagnostic tumour grading, pathologists
manually count the number of dividing cells (mitotic figures) in biopsy or
tumour resection specimens. Since the process is subjective and time-consuming,
data-driven artificial intelligence (AI) methods have been developed to
automatically detect mitotic figures. However, these methods often generalise
poorly, with performance reduced by variations in tissue types, staining
protocols, or the scanners used to digitise whole-slide images. Domain
adaptation approaches have been adopted in various applications to mitigate
this issue of domain shift. We evaluate two unsupervised domain adaptation
methods, CycleGAN and Neural Style Transfer, using the MIDOG 2021 Challenge
dataset. This challenge focuses on detecting mitotic figures in whole-slide
images digitised using different scanners. Two baseline mitosis detection
models based on U-Net and RetinaNet were investigated in combination with the
aforementioned domain adaptation methods. Both baseline models achieved human
expert level performance, but had reduced performance when evaluated on images
which had been digitised using a different scanner. The domain adaptation
techniques were each found to be beneficial for detection with data from some
scanners but not for others, with the only average increase across all scanners
being achieved by CycleGAN on the RetinaNet detector. These techniques require
further refinement to ensure consistency in mitosis detection.
