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Pulmonary hemorrhage (P-Hem) occurs among multiple species and can havevarious causes. Cytology of bronchoalveolarlavage fluid (BALF) using a 5-tierscoring system of alveolar macrophages based on their hemosiderin content isconsidered the most sensitive diagnostic method. We introduce a novel, fullyannotated multi-species P-Hem dataset which consists of 74 cytology whole slideimages (WSIs) with equine, feline and human samples. To create thishigh-quality and high-quantity dataset, we developed an annotation pipelinecombining human expertise with deep learning and data visualisation techniques.We applied a deep learning-based object detection approach trained on 17expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7feline WSIs. The resulting annotations were semi-automatically screened forerrors on multiple types of specialised annotation maps and finally reviewed bya trained pathologists. Our dataset contains a total of 297,383hemosiderophages classified into five grades. It is one of the largest publiclyavailableWSIs datasets with respect to the number of annotations, the scannedarea and the number of species covered.

Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens