WBCAtt

The examination of blood samples at a microscopic level plays a fundamental role in clinical diagnostics. 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 dataset 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). Note: this datasets is generated by joining the tranining, testing and validation sets of the original dataset without including the images to keep the tabular nature but dropping the columns ["img_name", "path"] since they are not needed for the tabular dataset. Please refer to https://github.com/apple2373/wbcatt/tree/main/submission for the original dataset.