  Mitotic activity is key for the assessment of malignancy in many tumors.
Moreover, it has been demonstrated that the proportion of abnormal mitosis to
normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can
be identified morphologically as having segregation abnormalities of the
chromatids. In this work, we perform, for the first time, automatic subtyping
of mitotic figures into normal and atypical categories according to
characteristic morphological appearances of the different phases of mitosis.
Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis
datasets, two experts blindly subtyped mitotic figures into five morphological
categories. Further, we set up a state-of-the-art object detection pipeline
extending the anchor-free FCOS approach with a gated hierarchical
subclassification branch. Our labeling experiment indicated that subtyping of
mitotic figures is a challenging task and prone to inter-rater disagreement,
which we found in 24.89% of MF. Using the more diverse MIDOG21 dataset for
training and TUPAC16 for testing, we reached a mean overall average precision
score of 0.552, a ROC AUC score of 0.833 for atypical/normal MF and a mean
class-averaged ROC-AUC score of 0.977 for discriminating the different phases
of cells undergoing mitosis.
