Observational studyScientific reports2022
A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm.
Observational study in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 123 citations in OpenAlex.
- Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.PloS one · 2025Pooled it
- Article
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- Detection-Guided ROI-Constrained Diffusion for Weakly Supervised White Blood Cell Segmentation: A Retrospective Internal and External Dataset Evaluation.Journal of clinical medicine · 2026Article
- Advancing WBC Classification: A Hybrid ConvNextV2-Swin Transformer Framework with R3GAN Data Balancing and CLAHE Preprocessing.Journal of imaging informatics in medicine · 2026Article
- Rethinking pathology image analysis through shuffling.npj biomedical innovations · 2026Article
- BloodContourNet: A YOLO-Based Density-Aware Bézier Refinement Framework for Peripheral Blood Cell Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Aligning deep learning and interpretable models for blood cell classification: A dual-model framework for explainability.PLOS digital health · 2026Article
- An expert-level vision-language model for multitask diagnostic morphology in clinical laboratories.NPJ digital medicine · 2026Article
- MurineCyto-Det: A High-Resolution Murine BALF Cytology Dataset for Leukocyte Segmentation and Detection.bioRxiv : the preprint server for biology · 2026Article
- GHMS-CycleGAN: Graph-Based Hierarchical Multi-stain CycleGAN for Stain Normalization and Classification in Digital Pathology.Journal of imaging informatics in medicine · 2026Article
- White blood cell classification using custom deep neural network and visualizing features of the images using heatmaps.Scientific reports · 2026Article
- A Large-Scale Peripheral Blood Cell Dataset for Automated Hematological Analysis.Scientific data · 2026Article
- Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell classification.MethodsX · 2025Article
- A large dataset of bone marrow cells in myelodysplastic syndrome for classification systems.Scientific data · 2025Article
- Gated Attention-Augmented Double U-Net for White Blood Cell Segmentation.Journal of imaging · 2025Article
- Test-Time Augmentation for Cross-Domain Leukocyte Classification via OOD Filtering and Self-Ensembling.Journal of imaging · 2025Article
- Biological Data Resources and Machine Learning Frameworks for Hematology Research.Genomics, proteomics & bioinformatics · 2025Review
- Multiscale deformed attention networks for white blood cell detection.Scientific reports · 2025Article
- Deep generative classification of blood cell morphology.Nature machine intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and early detection of anomalies in peripheral white blood cells plays a crucial role in the evaluation of well-being in individuals and the diagnosis and prognosis of hematologic diseases. For example, some blood disorders and immune system-related diseases are diagnosed by the differential count of white blood cells, which is one of the common laboratory tests. Data is one of the most important ingredients in the development and testing of many commercial and successful automatic or semi-automatic systems. To this end, this study introduces a free access dataset of normal peripheral white blood cells called Raabin-WBC containing about 40,000 images of white blood cells and color spots. For ensuring the validity of the data, a significant number of cells were labeled by two experts. Also, the ground truths of the nuclei and cytoplasm are extracted for 1145 selected cells. To provide the necessary diversity, various smears have been imaged, and two different cameras and two different microscopes were used. We did some preliminary deep learning experiments on Raabin-WBC to demonstrate how the generalization power of machine learning methods, especially deep neural networks, can be affected by the mentioned diversity. Raabin-WBC as a public data in the field of health can be used for the model development and testing in different machine learning tasks including classification, detection, segmentation, and localization.
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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.