# Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

arXiv: https://arxiv.org/abs/1908.04767 · HTML: https://mlsanity.com/paper/1908.04767/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 8 of 8)

- [Mitosis domain generalization in histopathology images -- The MIDOG challenge](https://mlsanity.com/paper/2204.03742/) (2022)
- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge](https://mlsanity.com/paper/2108.11269/) (2021)
- [Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images](https://mlsanity.com/paper/2109.00869/) (2021)
- [MitoDet: Simple and robust mitosis detection](https://mlsanity.com/paper/2109.01485/) (2021)
- [Learning to be EXACT, Cell Detection for Asthma on Partially Annotated Whole Slide Images](https://mlsanity.com/paper/2101.04943/) (2021)
- [Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens](https://mlsanity.com/paper/2108.08529/) (2021)
- [Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer](https://mlsanity.com/paper/2209.09193/) (2022)

## Abstract (arXiv)

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, μ=0.73, σ =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.