  Mitotic count (MC) is an important histological parameter for cancer
diagnosis and grading, but the manual process for obtaining MC from whole-slide
histopathological images is very time-consuming and prone to error. Therefore,
deep learning models have been proposed to facilitate this process. Existing
approaches utilize a two-stage pipeline: the detection stage for identifying
the locations of potential mitotic cells and the classification stage for
refining prediction confidences. However, this pipeline formulation can lead to
inconsistencies in the classification stage due to the poor prediction quality
of the detection stage and the mismatches in training data distributions
between the two stages. In this study, we propose a Refine Cascade Network
(ReCasNet), an enhanced deep learning pipeline that mitigates the
aforementioned problems with three improvements. First, window relocation was
used to reduce the number of poor quality false positives generated during the
detection stage. Second, object re-cropping was performed with another deep
learning model to adjust poorly centered objects. Third, improved data
selection strategies were introduced during the classification stage to reduce
the mismatches in training data distributions. ReCasNet was evaluated on two
large-scale mitotic figure recognition datasets, canine cutaneous mast cell
tumor (CCMCT) and canine mammary carcinoma (CMC), which resulted in up to 4.8%
percentage point improvements in the F1 scores for mitotic cell detection and
44.1% reductions in mean absolute percentage error (MAPE) for MC prediction.
Techniques that underlie ReCasNet can be generalized to other two-stage object
detection networks and should contribute to improving the performances of deep
learning models in broad digital pathology applications.
