  Breast cancer is one of the main causes of death worldwide. Histopathological
cellularity assessment of residual tumors in post-surgical tissues is used to
analyze a tumor's response to a therapy. Correct cellularity assessment
increases the chances of getting an appropriate treatment and facilitates the
patient's survival. In current clinical practice, tumor cellularity is manually
estimated by pathologists; this process is tedious and prone to errors or low
agreement rates between assessors. In this work, we evaluated three strong
novel Deep Learning-based approaches for automatic assessment of tumor
cellularity from post-treated breast surgical specimens stained with
hematoxylin and eosin. We validated the proposed methods on the BreastPathQ
SPIE challenge dataset that consisted of 2395 image patches selected from whole
slide images acquired from 64 patients. Compared to expert pathologist scoring,
our best performing method yielded the Cohen's kappa coefficient of 0.70 (vs.
0.42 previously known in literature) and the intra-class correlation
coefficient of 0.89 (vs. 0.83). Our results suggest that Deep Learning-based
methods have a significant potential to alleviate the burden on pathologists,
enhance the diagnostic workflow, and, thereby, facilitate better clinical
outcomes in breast cancer treatment.
