Update README.md

README.md

@@ -13,13 +13,193 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 303078398
     num_examples: 17092
-  download_size: 281772168
-  dataset_size: 303078398
 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
+
+```python
+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](https://chehablab.com) and cite the original PBC dataset:
+
+```bibtex
+@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:
+
+```bibtex
+@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.
+
+[![CC BY 4.0](https://licensebuttons.net/l/by/4.0/88x31.png)](https://creativecommons.org/licenses/by/4.0/)
+
+**Chehab Lab @ 2026**