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Related Experiment Video

Updated: May 31, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

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WBCAtt+: Fine-grained pixel-level morphological annotations for white blood cell images.

Satoshi Tsutsui1, Winnie Pang1, Shuting He2

  • 1Rapid-Rich Object Search (ROSE) Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

Medical Image Analysis
|May 28, 2026
PubMed
Summary

This study introduces WBCAtt+, a new dataset for white blood cell (WBC) image analysis. It offers detailed morphological attributes and cell components, advancing pathology research and enabling explainable AI.

Keywords:
AttributesExplainable AILeucocytesWhite blood cells

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Microscopic examination of white blood cells (WBCs) is crucial for diagnosing blood disorders.
  • Existing WBC image datasets lack detailed morphological annotations used by pathologists.
  • This limits the development of advanced diagnostic and analytical tools.

Purpose of the Study:

  • To introduce WBCAtt+, a comprehensive dataset for WBC image analysis.
  • To provide detailed annotations including 11 morphological attributes and 5 pixel-level cell components.
  • To facilitate research in explainable AI for pathology.

Main Methods:

  • Developed WBCAtt+, a novel dataset with 113k image-level labels and 10k segmentation maps.
  • Created baseline models for attribute recognition and semantic segmentation using the dataset.
  • Designed an attribute recognition model incorporating compositional cell structures.

Main Results:

  • WBCAtt+ offers the most comprehensive annotations for WBC images to date.
  • Baseline models demonstrate the utility of the dataset for attribute recognition and segmentation.
  • The compositional attribute recognition model shows improved performance.

Conclusions:

  • WBCAtt+ addresses the gap in detailed WBC image annotations.
  • The dataset enables advanced applications like explainable AI and counterfactual generation.
  • Public availability of the dataset and code promotes further research in computational pathology.