  Each woman living in the United States has about 1 in 8 chance of developing
invasive breast cancer. The mitotic cell count is one of the most common tests
to assess the aggressiveness or grade of breast cancer. In this prognosis,
histopathology images must be examined by a pathologist using high-resolution
microscopes to count the cells. Unfortunately, this can be an exhaustive task
with poor reproducibility, especially for non-experts. Deep learning networks
have recently been adapted to medical applications which are able to
automatically localize these regions of interest. However, these region-based
networks lack the ability to take advantage of the segmentation features
produced by a full image CNN which are often used as a sole method of
detection. Therefore, the proposed method leverages Faster RCNN for object
detection while fusing segmentation features generated by a UNet with RGB image
features to achieve an F-score of 0.508 on the MITOS-ATYPIA 2014 mitosis
counting challenge dataset, outperforming state-of-the-art methods.
