This is the official repository for the WBCAtt dataset.

• Dataset and code is here.
• Satoshi Tsutsui, Winnie Pang, and Bihan Wen. WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes. Advances in Neural Information Processing Systems (NeurIPS) 2023.
• We've annotated 113k labels (11 attributes x 10.3k images) on white blood cell images, detailing the fine-grained concepts pathologists recognize. Annotating at this level of detail and scale is unprecedented, offering unique value to AI in pathology. Please refere to the paper below for more details.

Reference

If you find this code/data useful, please consider to cide:

Journal Version

• Satoshi Tsutsui, Winnie Pang, Shuting He, and Bihan Wen, “WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images,” Medical Image Analysis, 2026.
• Arxiv: http://arxiv.org/abs/2605.19692
• Abstract: The microscopic examination of white blood cells (WBCs) plays a fundamental role in pathology and is essential for diagnosing blood disorders such as leukemia and anemia. To support further research on WBC images, multiple datasets have been proposed. However, they mainly annotate cell categories, and lack detailed morphological characteristics that pathologists use to explain their interpretations of cells. To address this gap, we introduce WBCAtt+, a novel dataset of WBC images densely annotated with 11 morphological attributes and five pixel-level cell components. With 113k image-level labels and 10k segmentation maps, WBCAtt+ is the first to provide comprehensive annotations for WBC images. Leveraging this dataset, we provide baseline models for attribute recognition and semantic segmentation. We also design an attribute recognition model to incorporate compositional structure of cells, further improving the recognition performance. Lastly, we showcase various applications enabled by our dataset, such as explainable AI models, including counterfactual example generation. The dataset and code are publicly available.

@article{tsutsui2026wbcattplus,
  title={WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images},
  author={Tsutsui, Satoshi and Pang, Winnie and He, Shuting and Wen, Bihan},
  journal={Medical Image Analysis},
  year={2026}
}

Conference Version (this repository)

• Satoshi Tsutsui, Winnie Pang, and Bihan Wen. WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes. Advances in Neural Information Processing Systems (NeurIPS) 2023.
• Arxiv: https://arxiv.org/abs/2306.13531
• Abstract: 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.

@inproceedings{tsutsui2023wbcatt,
  title={WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes},
  author={Tsutsui, Satoshi and Pang, Winnie and Wen, Bihan},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS).},
  year={2023}
}