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

arXiv: https://arxiv.org/abs/2108.08529 · HTML: https://mlsanity.com/paper/2108.08529/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types](https://mlsanity.com/paper/2002.07913/) (2020)
- [EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version control](https://mlsanity.com/paper/2004.14595/) (2020)
- [Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides](https://mlsanity.com/paper/1908.04767/) (2019)
- [Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides](https://mlsanity.com/paper/2004.05838/) (2020)

## Abstract (arXiv)

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.