  Purpose: Exercise-induced pulmonary hemorrhage (EIPH) is a common syndrome in
sport horses with negative impact on performance. Cytology of bronchoalveolar
lavage fluid by use of a scoring system is considered the most sensitive
diagnostic method. Macrophages are classified depending on the degree of
cytoplasmic hemosiderin content. The current gold standard is manual grading,
which is however monotonous and time-consuming. Methods: We evaluated
state-of-the-art deep learning-based methods for single cell macrophage
classification and compared them against the performance of nine cytology
experts and evaluated inter- and intra-observer variability. Additionally, we
evaluated object detection methods on a novel data set of 17 completely
annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages.
Resultsf: Our deep learning-based approach reached a concordance of 0.85,
partially exceeding human expert concordance (0.68 to 0.86, $\mu$=0.73,
$\sigma$ =0.04). Intra-observer variability was high (0.68 to 0.88) and
inter-observer concordance was moderate (Fleiss kappa = 0.67). Our object
detection approach has a mean average precision of 0.66 over the five classes
from the whole slide gigapixel image and a computation time of below two
minutes. Conclusion: To mitigate the high inter- and intra-rater variability,
we propose our automated object detection pipeline, enabling accurate,
reproducible and quick EIPH scoring in WSI.
