  Mitotic figure detection in histology images is a hard-to-define, yet
clinically significant task, where labels are generated with pathologist
interpretations and where there is no ``gold-standard'' independent
ground-truth. However, it is well-established that these interpretation based
labels are often unreliable, in part, due to differences in expertise levels
and human subjectivity. In this paper, our goal is to shed light on the
inherent uncertainty of mitosis labels and characterize the mitotic figure
classification task in a human interpretable manner. We train a probabilistic
diffusion model to synthesize patches of cell nuclei for a given mitosis label
condition. Using this model, we can then generate a sequence of synthetic
images that correspond to the same nucleus transitioning into the mitotic
state. This allows us to identify different image features associated with
mitosis, such as cytoplasm granularity, nuclear density, nuclear irregularity
and high contrast between the nucleus and the cell body. Our approach offers a
new tool for pathologists to interpret and communicate the features driving the
decision to recognize a mitotic figure.
