  Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have
various causes. Cytology of bronchoalveolarlavage fluid (BALF) using a 5-tier
scoring system of alveolar macrophages based on their hemosiderin content is
considered the most sensitive diagnostic method. We introduce a novel, fully
annotated multi-species P-Hem dataset which consists of 74 cytology whole slide
images (WSIs) with equine, feline and human samples. To create this
high-quality and high-quantity dataset, we developed an annotation pipeline
combining human expertise with deep learning and data visualisation techniques.
We applied a deep learning-based object detection approach trained on 17
expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7
feline WSIs. The resulting annotations were semi-automatically screened for
errors on multiple types of specialised annotation maps and finally reviewed by
a trained pathologists. Our dataset contains a total of 297,383
hemosiderophages classified into five grades. It is one of the largest publicly
availableWSIs datasets with respect to the number of annotations, the scanned
area and the number of species covered.
