The examination of blood samples at a microscopic level plays a fundamental role in clinical diagnostics, influencing a wide range of medical conditions. For instance, an in-depth study of White Blood Cells (WBCs), a crucial component of our blood, is essential for diagnosing blood-related diseases such as leukemia and anemia. While multiple datasets containing WBC images have been proposed, they mostly focus on cell categorization, often lacking the necessary morphological details to explain such categorizations, despite the importance of explainable artificial intelligence (XAI) in medical domains. This paper seeks to address this limitation by introducing comprehensive annotations for WBC images. Through collaboration with pathologists, a thorough literature review, and manual inspection of microscopic images, we have identified 11 morphological attributes associated with the cell and its components (nucleus, cytoplasm, and granules). We then annotated ten thousand WBC images with these attributes. Moreover, we conduct experiments to predict these attributes from images, providing insights beyond basic WBC classification. As the first public dataset to offer such extensive annotations, we also illustrate specific applications that can benefit from our attribute annotations. Overall, our dataset paves the way for interpreting WBC recognition models, further advancing XAI in the fields of pathology and hematology.

翻译：血液样本的显微镜检查在临床诊断中发挥着基础性作用，影响着广泛的医学状况。例如，深入研究白细胞（WBCs）这一血液关键组分，对于诊断白血病和贫血等血液相关疾病至关重要。尽管已有多个包含白细胞图像的公开数据集，但它们大多聚焦于细胞分类，往往缺乏解释此类分类所需的形态学细节，尽管可解释人工智能（XAI）在医学领域具有重要意义。本文旨在通过引入白细胞的全面注释来弥补这一局限。在与病理学家合作、进行全面的文献综述以及手动检查显微镜图像的基础上，我们识别出与细胞及其组成部分（细胞核、细胞质和颗粒）相关的11项形态属性。随后，我们使用这些属性对一万张白细胞图像进行了标注。此外，我们开展了从图像中预测这些属性的实验，提供了超越基础白细胞分类的见解。作为首个提供如此大规模注释的公开数据集，我们还展示了能够从我们的属性注释中受益的具体应用。总体而言，我们的数据集为解读白细胞识别模型铺平了道路，进一步推动了病理学和血液学领域的可解释人工智能发展。