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The examination of blood samples at a microscopic level plays a fundamentalrole in clinical diagnostics, influencing a wide range of medical conditions.For instance, an in-depth study of White Blood Cells (WBCs), a crucialcomponent of our blood, is essential for diagnosing blood-related diseases suchas leukemia and anemia. While multiple datasets containing WBC images have beenproposed, they mostly focus on cell categorization, often lacking the necessarymorphological details to explain such categorizations, despite the importanceof explainable artificial intelligence (XAI) in medical domains. This paperseeks to address this limitation by introducing comprehensive annotations forWBC images. Through collaboration with pathologists, a thorough literaturereview, and manual inspection of microscopic images, we have identified 11morphological attributes associated with the cell and its components (nucleus,cytoplasm, and granules). We then annotated ten thousand WBC images with theseattributes. Moreover, we conduct experiments to predict these attributes fromimages, providing insights beyond basic WBC classification. As the first publicdataset to offer such extensive annotations, we also illustrate specificapplications that can benefit from our attribute annotations. Overall, ourdataset paves the way for interpreting WBC recognition models, furtheradvancing XAI in the fields of pathology and hematology.

WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes