  Manual counting of mitotic tumor cells in tissue sections constitutes one of
the strongest prognostic markers for breast cancer. This procedure, however, is
time-consuming and error-prone. We developed a method to automatically detect
mitotic figures in breast cancer tissue sections based on convolutional neural
networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained
histological tissue sections is hampered by: (1) noisy and expensive reference
standards established by pathologists, (2) lack of generalization due to
staining variation across laboratories, and (3) high computational requirements
needed to process gigapixel whole-slide images (WSIs). In this paper, we
present a method to train and evaluate CNNs to specifically solve these issues
in the context of mitosis detection in breast cancer WSIs. First, by combining
image analysis of mitotic activity in phosphohistone-H3 (PHH3) restained slides
and registration, we built a reference standard for mitosis detection in entire
H&E WSIs requiring minimal manual annotation effort. Second, we designed a data
augmentation strategy that creates diverse and realistic H&E stain variations
by modifying the hematoxylin and eosin color channels directly. Using it during
training combined with network ensembling resulted in a stain invariant mitosis
detector. Third, we applied knowledge distillation to reduce the computational
requirements of the mitosis detection ensemble with a negligible loss of
performance. The system was trained in a single-center cohort and evaluated in
an independent multicenter cohort from The Cancer Genome Atlas on the three
tasks of the Tumor Proliferation Assessment Challenge (TUPAC). We obtained a
performance within the top-3 best methods for most of the tasks of the
challenge.
