  Mitotic counts are one of the key indicators of breast cancer prognosis.
However, accurate mitotic cell counting is still a difficult problem and is
labourious. Automated methods have been proposed for this task, but are usually
dependent on the training images and show poor performance on unseen domains.
In this work, we present a multi-stage mitosis detection method based on a
Cascade RCNN developed to be sequentially more selective against false
positives. On the preliminary test set, the algorithm scores an F1-score of
0.7492.
