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Shetab Boushehri, S. ; Kazeminia, S. ; Gruber, A. ; Matek, C. ; Spiekermann, K.* ; Pohlkamp, C.* ; Haferlach, T.* ; Marr, C.

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

Sci. Data 12:1773 (2025)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 2052-4463
e-ISSN 2052-4463
Zeitschrift Scientific Data
Quellenangaben Band: 12, Heft: 1, Seiten: , Artikelnummer: 1773 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Human-Centered AI (HCA)