  The count of mitotic figures (MFs) observed in hematoxylin and eosin
(H&E)-stained slides is an important prognostic marker as it is a measure for
tumor cell proliferation. However, the identification of MFs has a known low
inter-rater agreement. Deep learning algorithms can standardize this task, but
they require large amounts of annotated data for training and validation.
Furthermore, label noise introduced during the annotation process may impede
the algorithm's performance. Unlike H&E, the mitosis-specific antibody
phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides
stained against PHH3 leads to higher agreement among raters and has therefore
recently been used as a ground truth for the annotation of MFs in H&E. However,
as PHH3 facilitates the recognition of cells indistinguishable from H&E stain
alone, the use of this ground truth could potentially introduce noise into the
H&E-related dataset, impacting model performance. This study analyzes the
impact of PHH3-assisted MF annotation on inter-rater reliability and object
level agreement through an extensive multi-rater experiment. We found that the
annotators' object-level agreement increased when using PHH3-assisted labeling.
Subsequently, MF detectors were evaluated on the resulting datasets to
investigate the influence of PHH3-assisted labeling on the models' performance.
Additionally, a novel dual-stain MF detector was developed to investigate the
interpretation-shift of PHH3-assisted labels used in H&E, which clearly
outperformed single-stain detectors. However, the PHH3-assisted labels did not
have a positive effect on solely H&E-based models. The high performance of our
dual-input detector reveals an information mismatch between the H&E and
PHH3-stained images as the cause of this effect.
