Datasets:

chehablab
/

PBC

dataset_info:
  features:
    - name: image_id
      dtype: string
    - name: image
      dtype: image
    - name: cell_type
      dtype: string
    - name: cell_subtype
      dtype: string
    - name: label
      dtype: int32
  splits:
    - name: train
      num_examples: 17092
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - image-classification
language:
  - en
tags:
  - microscopy
  - blood
  - hematology
  - blood-cells
  - white-blood-cells
  - medical
  - classification
pretty_name: PBC (Peripheral Blood Cells)
size_categories:
  - 10K<n<100K

PBC (Peripheral Blood Cells) — Microscopic Images of Normal Peripheral Blood Cells

This dataset is derived from the PBC dataset (Acevedo et al., 2020), a collection of microscopic images of individual normal peripheral blood cells, originally hosted on Mendeley Data. The original images were acquired with the CellaVision DM96 analyzer in the core laboratory of the Hospital Clinic of Barcelona and labelled by expert clinical pathologists into eight cell groups. Here, we kept the original JPG images unchanged and added the cell type, the cell subtype from the image filename, and an integer label. The dataset is designed for blood-cell classification.

📦 Dataset Structure

The dataset has a single train split, with each row corresponding to a single blood-cell image:

Field	Description
`image_id`	Original image filename without extension (e.g., `BA_100102`)
`image`	RGB microscopy image of a single cell (360 × 363 pixels, JPG)
`cell_type`	Cell group: `basophil`, `eosinophil`, `erythroblast`, `ig`, `lymphocyte`, `monocyte`, `neutrophil`, `platelet`
`cell_subtype`	Filename prefix of the original image (e.g., `SNE`, `BNE`, `MY`), which encodes finer subtypes for some groups
`label`	Integer cell group: `0`–`7` (see Feature Descriptions)

Class Distribution:

Basophil: 1,218 images
Eosinophil: 3,117 images
Erythroblast: 1,551 images
Immature granulocyte (IG): 2,895 images
Lymphocyte: 1,214 images
Monocyte: 1,420 images
Neutrophil: 3,329 images
Platelet: 2,348 images
Total: 17,092 images

⚙️ Preprocessing

PBC_dataset_normal_DIB.zip was downloaded from Mendeley Data (snkd93bnjr, version 1)
Images were kept as the original JPG files (no re-encoding, resizing or color normalization)
Cell type was taken from each image's parent folder name
Cell subtype was taken from the image filename prefix (the part before the last _)
All images were preserved

🚀 Usage

from datasets import load_dataset
import matplotlib.pyplot as plt

ds = load_dataset("chehablab/PBC", split="train")

# One example image per cell group
classes = ["basophil", "eosinophil", "erythroblast", "ig",
           "lymphocyte", "monocyte", "neutrophil", "platelet"]
labels = ds["label"]
fig, axes = plt.subplots(1, len(classes), figsize=(16, 2.6))
for ax, label in zip(axes, range(len(classes))):
    sample = ds[labels.index(label)]
    ax.imshow(sample["image"])
    ax.set_title(f"{classes[label]}\n{sample['cell_subtype']}")
    ax.axis("off")
plt.tight_layout()
plt.show()

# Filter by cell group
neutrophils = ds.filter(lambda x: x["cell_type"] == "neutrophil")
print(f"Neutrophils: {len(neutrophils)}")

# No official split: make a stratified train/test split
split = ds.class_encode_column("label").train_test_split(test_size=0.2, stratify_by_column="label", seed=0)

🏷️ Feature Descriptions

Cell Groups

Eight groups of normal peripheral blood cells:

Basophil (label=0): 1,218 images — Granulocyte with large dark-purple granules
Eosinophil (label=1): 3,117 images — Granulocyte with orange-red granules
Erythroblast (label=2): 1,551 images — Nucleated red blood cell precursor
Immature granulocyte, IG (label=3): 2,895 images — Promyelocytes, myelocytes and metamyelocytes
Lymphocyte (label=4): 1,214 images — Mononuclear cell with a round nucleus and scant cytoplasm
Monocyte (label=5): 1,420 images — Large mononuclear cell with a folded or kidney-shaped nucleus
Neutrophil (label=6): 3,329 images — Segmented and band neutrophils
Platelet (label=7): 2,348 images — Platelets (thrombocytes)

Image Specifications

Modality: Brightfield microscopy of peripheral blood smears
Stain: May Grünwald-Giemsa
Acquisition: CellaVision DM96 automatic analyzer
Image size: 360 × 363 pixels, RGB, JPG
Content: One centered cell per image

Acquisition Protocol

All images were acquired at:

Core Laboratory, Hospital Clinic of Barcelona, Spain
Subjects: Individuals without infection, hematologic or oncologic disease, and free of any pharmacological treatment at the time of blood collection
Time period: 2015 – 2019

🧪 Use Cases

Multi-class classification of normal peripheral blood cells
White-blood-cell differential counting
Fine-grained classification of granulocyte maturation stages (via cell_subtype)
Representation learning and feature extraction for hematology microscopy
Self-supervised pretraining on blood-cell images
Reference set of normal cells for abnormal-cell or leukemia detection studies

⚠️ Important Notes

No official split: The dataset has a single train split. Make your own stratified train/validation/test split (see Usage).
No patient IDs: The original release does not say which images come from the same patient, so a random split can place cells from one smear in both training and evaluation.
Class imbalance: Neutrophils (3,329) and eosinophils (3,117) are about 2.7× more frequent than lymphocytes (1,214) and basophils (1,218). Use weighted sampling or class balancing as needed.
cell_subtype: This field is read from the original filename prefix and is not an official label; check its values (ds.to_pandas().groupby(["cell_type", "cell_subtype"]).size()) before using it.
Normal cells only: All images come from healthy individuals; the dataset contains no pathological (e.g., leukemic) cells.

📚 Citation

This dataset is derived from data originally published on Mendeley Data under a CC BY 4.0 license. If you use this dataset, please acknowledge Chehab Lab and cite the original PBC dataset:

@misc{Acevedo2020PBCData,
  author    = {Acevedo, Andrea and Merino, Anna and Alf{\'e}rez, Santiago and Molina, {\'A}ngel and Bold{\'u}, Laura and Rodellar, Jos{\'e}},
  title     = {A dataset for microscopic peripheral blood cell images for development of automatic recognition systems [dataset]},
  year      = {2020},
  publisher = {Mendeley Data},
  version   = {1},
  doi       = {10.17632/snkd93bnjr.1}
}

Additionally, cite the research paper that introduced this dataset:

@article{Acevedo2020PBC,
  author  = {Acevedo, Andrea and Merino, Anna and Alf{\'e}rez, Santiago and Molina, {\'A}ngel and Bold{\'u}, Laura and Rodellar, Jos{\'e}},
  title   = {A dataset of microscopic peripheral blood cell images for development of automatic recognition systems},
  journal = {Data in Brief},
  volume  = {30},
  pages   = {105474},
  year    = {2020},
  doi     = {10.1016/j.dib.2020.105474}
}

✉️ Original Authors

Andrea Acevedo, Anna Merino, Santiago Alférez, Ángel Molina, Laura Boldú, José Rodellar
Core Laboratory, Biochemistry and Molecular Genetics Department, Hospital Clinic of Barcelona, and Department of Mathematics, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Mendeley Data: https://data.mendeley.com/datasets/snkd93bnjr/1 (original files)

📜 License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, consistent with the original PBC dataset.

You may copy, modify, distribute, and use the data for any purpose (commercial or non-commercial), provided that appropriate credit is given to Acevedo et al., Mendeley Data, and the original paper.

Chehab Lab @ 2026