  The effective localization of mitosis is a critical precursory task for
deciding tumor prognosis and grade. Automated mitosis detection through deep
learning-oriented image analysis often fails on unseen patient data due to
inherent domain biases. This paper proposes a domain homogenizer for mitosis
detection that attempts to alleviate domain differences in histology images via
adversarial reconstruction of input images. The proposed homogenizer is based
on a U-Net architecture and can effectively reduce domain differences commonly
seen with histology imaging data. We demonstrate our domain homogenizer's
effectiveness by observing the reduction in domain differences between the
preprocessed images. Using this homogenizer, along with a subsequent retina-net
object detector, we were able to outperform the baselines of the 2021 MIDOG
challenge in terms of average precision of the detected mitotic figures.
