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<p>Distinguishing cell types in a&#xa0;peripheral blood smear is critical for diagnosing blood diseases, such as leukemia subtypes. Artificial intelligence can assist in automating cell classification. For training robust machine learning algorithms, however, large and well-annotated single-cell datasets are pivotal. Here, we introduce a large, publicly available, annotated peripheral blood dataset comprising &gt;40,000 single-cell images classified into 18 classes by cytomorphology experts from the Munich Leukemia Laboratory, the largest European laboratory for blood disease diagnostics. By making our dataset publicly available, we provide a valuable resource for medical and machine learning researchers and support the development of reliable and clinically relevant diagnostic tools for diagnosing hematological diseases.</p>

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A large expert-annotated single-cell peripheral blood dataset for hematological disease diagnostics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月11日

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  • Sayedali Shetab Boushehri,
  • Salome Kazeminia,
  • Armin Gruber,
  • Christian Matek,
  • Karsten Spiekermann,
  • Christian Pohlkamp,
  • Torsten Haferlach,
  • Carsten Marr

摘要

Distinguishing cell types in a peripheral blood smear is critical for diagnosing blood diseases, such as leukemia subtypes. Artificial intelligence can assist in automating cell classification. For training robust machine learning algorithms, however, large and well-annotated single-cell datasets are pivotal. Here, we introduce a large, publicly available, annotated peripheral blood dataset comprising >40,000 single-cell images classified into 18 classes by cytomorphology experts from the Munich Leukemia Laboratory, the largest European laboratory for blood disease diagnostics. By making our dataset publicly available, we provide a valuable resource for medical and machine learning researchers and support the development of reliable and clinically relevant diagnostic tools for diagnosing hematological diseases.