ArticleScientific data2023
A high-resolution large-scale dataset of pathological and normal white blood cells.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- MurineCyto-Det: A High-Resolution Murine BALF Cytology Dataset for Leukocyte Segmentation and Detection.bioRxiv : the preprint server for biology · 2026Article
- A Large-Scale Peripheral Blood Cell Dataset for Automated Hematological Analysis.Scientific data · 2026Article
- Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability.Frontiers in medicine · 2026Article
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- A large dataset of bone marrow cells in myelodysplastic syndrome for classification systems.Scientific data · 2025Article
- Improved leukocyte classification in bone marrow cytology using convolutional neural network with contrast enhancement.Scientific reports · 2025Article
- Leveraging a foundation model zoo for cell similarity search in oncological microscopy across devices.Frontiers in oncology · 2025Article
- Deep generative classification of blood cell morphology.Nature machine intelligence · 2025Article
- A large multi-focus dataset for white blood cell classification.Scientific data · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microscopic examination plays a significant role in the initial screening for a variety of hematological, as well as non-hematological, diagnoses. Microscopic blood smear examination that is considered a key diagnostic technique, is in recent clinical practice still performed manually, which is not only time consuming, but can lead to human errors. Although automated and semi-automated systems have been developed in recent years, their high purchasing and maintenance costs make them unaffordable for many medical institutions. Even though much research has been conducted lately to explore more accurate and feasible solutions, most researchers had to deal with a lack of medical data. To address the lack of large-scale databases in this field, we created a high-resolution dataset containing a total of 16027 annotated white blood cells. Moreover, the dataset covers overall 9 types of white blood cells, including clinically significant pathological findings. Since we used high-quality acquisition equipment, the dataset provides one of the highest quality images of blood cells, achieving an approximate resolution of 42 pixels per 1 μm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.