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. 2025 Nov 11;12(1):1773.
doi: 10.1038/s41597-025-06223-x.

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

Sayedali Shetab Boushehri #  1   2   3 , Salome Kazeminia #  1   3 , Armin Gruber  1   4 , Christian Matek  1 , Karsten Spiekermann  4   5   6 , Christian Pohlkamp  7 , Torsten Haferlach  7 , Carsten Marr  8   9   10   11
Affiliations

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

Sayedali Shetab Boushehri et al. Sci Data. .

Abstract

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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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
A fully annotated single-cell peripheral blood dataset. (a) Workflow of generating the imaging dataset at the Munich Leukemia Laboratory. (b) The MLL23 dataset contains 18 classes with varying numbers of images per class. Ten representative images per class are depicted to provide an overview of the dataset.
See this image and copyright information in PMC

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