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

本数据集名为‘Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens’，由德国弗里德里希-亚历山大-埃尔兰根-纽伦堡大学的模式识别实验室主导创建。数据集包含74个全切片图像，涵盖马、人和猫的肺含铁血黄素吞噬细胞样本，总计297,383个含铁血黄素吞噬细胞分类为五个等级。创建过程中，结合了人类专家知识和深度学习技术，通过半自动错误筛查和专家最终审查确保数据质量。该数据集主要应用于跨物种深度学习模型的开发，旨在解决不同物种间肺出血诊断的准确性和一致性问题。

The dataset is named 'Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens'. It was led and developed by the Pattern Recognition Lab at Friedrich-Alexander University Erlangen-Nuremberg, Germany. The dataset consists of 74 whole-slide images of pulmonary hemosiderophage samples from equine, human and feline specimens, with a total of 297,383 hemosiderophages annotated into five categories. During its construction, human expert knowledge and deep learning technologies were combined, and data quality was ensured through semi-automated error screening and final expert review. This dataset is primarily applied to the development of cross-species deep learning models, aiming to address the challenges of achieving accurate and consistent pulmonary hemorrhage diagnosis across different species.