摘要：Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fuid (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 fnally reviewed by a trained pathologist . Our dataset contains a total of 297,383 hemosiderophages classifed into fve grades . It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.
