  Nuclear pleomorphism, defined herein as the extent of abnormalities in the
overall appearance of tumor nuclei, is one of the components of the
three-tiered breast cancer grading. Given that nuclear pleomorphism reflects a
continuous spectrum of variation, we trained a deep neural network on a large
variety of tumor regions from the collective knowledge of several pathologists,
without constraining the network to the traditional three-category
classification. We also motivate an additional approach in which we discuss the
additional benefit of normal epithelium as baseline, following the routine
clinical practice where pathologists are trained to score nuclear pleomorphism
in tumor, having the normal breast epithelium for comparison. In multiple
experiments, our fully-automated approach could achieve top pathologist-level
performance in select regions of interest as well as at whole slide images,
compared to ten and four pathologists, respectively.
