WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
This provides a new resource for researchers in pathology and hematology to enhance interpretability of WBC recognition models, though it is incremental as it builds on existing datasets by adding annotations.
The authors tackled the lack of detailed morphological annotations in white blood cell (WBC) image datasets by introducing WBCAtt, a dataset with 10,000 images annotated with 11 attributes, enabling insights beyond basic classification for improved explainable AI in medical diagnostics.
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.