339 research outputs found
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes
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
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