This dataset is an anonymized blood cell dataset released in 2025 by a research team at the University of Cambridge, UK. The related research paper is titled "...".Deep generative classification of blood cell morphology", has been published in Nature.

This dataset contains 2,904 blood smears from Addenbrooke's Hospital in Cambridge, comprising a total of 559,808 single-cell images. Of these, 4,996 images are labeled with ten blood cell types, including erythroblasts, eosinophils, monocytes, and immature cells. The images were acquired using a CellaVision DM9600 system, and the annotations include confidence scores from each annotation expert, providing important reference for subsequent uncertainty quantification. CytoData also includes artifact categories to address common non-cellular structural interference in blood smears, which is of significant value in clinical applications.