  Empirical evaluation of breast tissue biopsies for mitotic nuclei detection
is considered an important prognostic biomarker in tumor grading and cancer
progression. However, automated mitotic nuclei detection poses several
challenges because of the unavailability of pixel-level annotations, different
morphological configurations of mitotic nuclei, their sparse representation,
and close resemblance with non-mitotic nuclei. These challenges undermine the
precision of the automated detection model and thus make detection difficult in
a single phase. This work proposes an end-to-end detection system for mitotic
nuclei identification in breast cancer histopathological images. Deep object
detection-based Mask R-CNN is adapted for mitotic nuclei detection that
initially selects the candidate mitotic region with maximum recall. However, in
the second phase, these candidate regions are refined by multi-object loss
function to improve the precision. The performance of the proposed detection
model shows improved discrimination ability (F-score of 0.86) for mitotic
nuclei with significant precision (0.86) as compared to the two-stage detection
models (F-score of 0.701) on TUPAC16 dataset. Promising results suggest that
the deep object detection-based model has the potential to learn the
characteristic features of mitotic nuclei from weakly annotated data and
suggests that it can be adapted for the identification of other nuclear bodies
in histopathological images.
