README.md

---
license: cc-by-nc-4.0
task_categories:
  - image-classification
language:
  - en
tags:
  - medical
  - hematology
  - white-blood-cell
  - wbc
  - robustness
  - class-imbalance
  - isbi-2026
pretty_name: WBCBench 2026 - Robust White Blood Cell Classification
size_categories:
  - 100K<n<1M
configs:
  - config_name: pristine
    data_files:
      - split: phase1_train
        path: pristine/phase1_train-*.parquet
      - split: phase2_eval
        path: pristine/phase2_eval-*.parquet
      - split: phase2_test
        path: pristine/phase2_test-*.parquet
      - split: phase2_train
        path: pristine/phase2_train-*.parquet
    features:
      - name: image
        dtype: image
      - name: image_id
        dtype: string
      - name: label
        dtype: string
      - name: wbcbench_split
        dtype: string
      - name: severity
        dtype: string
      - name: original_image_id
        dtype: string
      - name: patient_hash
        dtype: string
  - config_name: degraded
    data_files:
      - split: phase2_eval
        path: degraded/phase2_eval-*.parquet
      - split: phase2_test
        path: degraded/phase2_test-*.parquet
      - split: phase2_train
        path: degraded/phase2_train-*.parquet
    features:
      - name: image
        dtype: image
      - name: image_id
        dtype: string
      - name: label
        dtype: string
      - name: wbcbench_split
        dtype: string
      - name: severity
        dtype: string
      - name: original_image_id
        dtype: string
      - name: patient_hash
        dtype: string

extra_gated_heading: "WBCBench 2026 Data Use Agreement"
extra_gated_description: "This dataset contains anonymized clinical white-blood-cell images used in the WBCBench 2026 ISBI challenge. Access requires accepting the data-use agreement below; requests are reviewed manually by the WBCBench 2026 team."
extra_gated_prompt: |
  By requesting access, you agree to the following:

  1. You will use this dataset for **non-commercial research only** (CC BY-NC 4.0).
  2. You will **cite the WBCBench 2026 paper** (Tian et al., ISBI 2026) in any publication that uses this dataset.
  3. You will **not attempt to re-identify** any patient in the data, nor link it to external clinical records.
  4. You will **not redistribute** the dataset or share your access with third parties.
  5. You will **not use this dataset to train commercial products or services** (model training intended for commercial deployment is prohibited).
  6. You will **report any data leak or unauthorized access** to the WBCBench 2026 team.
  7. Approval is **manual**.

extra_gated_fields:
  Full Name: text
  Contact Email: text
  Affiliation or Organization: text
  Department or Lab: text
  Position:
    type: select
    options:
      - Undergraduate
      - Masters student
      - PhD student
      - Postdoc
      - Faculty
      - Industry researcher
      - Clinician
      - Other
  Supervisor or PI name (optional, recommended if student/postdoc):
    type: text
    required: false
  Country: text
  IRB or Ethics approval reference (optional):
    type: text
    required: false
  Intended use (project or paper title and brief description): text
  I agree to non-commercial use only: checkbox
  I will cite the WBCBench 2026 paper: checkbox
  I will not attempt to re-identify any patient: checkbox
  I will not redistribute or share access with third parties: checkbox
  I will not use this dataset for commercial product training: checkbox
  I will report any data leak or unauthorized access: checkbox

extra_gated_button_content: "Request access"
---

# WBCBench 2026 - Robust White Blood Cell Classification Under Class Imbalance

Dataset for the [WBCBench 2026 ISBI Challenge](https://www.kaggle.com/competitions/wbc-bench-2026/overview).
A 13-class white-blood-cell classification benchmark with mixed pristine and degraded images for
robustness evaluation under realistic imaging conditions.

- **Paper:** [arXiv:2604.10797](https://arxiv.org/abs/2604.10797)
- **Challenge website:** https://xudong-ma.github.io/WBCBench2026-Robust-White-Blood-Cell-Classification/
- **Kaggle competition:** https://www.kaggle.com/competitions/wbc-bench-2026/overview
- **License (data):** CC BY-NC 4.0
- **License (scripts in `scripts/`):** MIT

## Access

This dataset is **gated**. Click the **Request access** button on this page, fill the form,
and accept the data-use agreement. Requests are reviewed manually by the WBCBench 2026 team.

## Cell type labels

The `label` column uses these 13 abbreviations:

| Abbreviation | Cell type |
|---|---|
| **SNE** | Segmented neutrophil |
| **LY** | Lymphocyte |
| **MO** | Monocyte |
| **EO** | Eosinophil |
| **BA** | Basophil |
| **VLY** | Variant (atypical) lymphocyte |
| **BNE** | Band-form neutrophil |
| **MMY** | Metamyelocyte |
| **MY** | Myelocyte |
| **PMY** | Promyelocyte |
| **BL** | Blast cell |
| **PC** | Plasma cell |
| **PLY** | Prolymphocyte |

Also available as a machine-readable file at `metadata/class_legend.csv`.

## Configs and splits

| Config | Split | Shards |
|---|---|---:|
| degraded | phase2_eval | 1 shards |
| degraded | phase2_test | 2 shards |
| degraded | phase2_train | 4 shards |
| pristine | phase1_train | 1 shards |
| pristine | phase2_eval | 1 shards |
| pristine | phase2_test | 1 shards |
| pristine | phase2_train | 1 shards |

Splits are stored as parquet shards (about 500 MB each). Each shard is named `<split>-<NNNNN>-of-<MMMMM>.parquet`
(e.g., `phase2_train-00002-of-00004.parquet` = shard 2 of 4). The `degraded` config has more shards because
noisy/blurred JPEGs compress less efficiently (about 85 KB/image vs about 20 KB pristine), not because the
row count differs.

## Schema

Every row contains:

| Column | Type | Notes |
|---|---|---|
| `image` | Image | The JPEG bytes, decoded as PIL by `datasets` |
| `image_id` | str | Filename stem (e.g., `"00173214"` or `"01416766"`) |
| `label` | str | One of 13 classes: BA, BL, BNE, EO, LY, MMY, MO, MY, PC, PLY, PMY, SNE, VLY |
| `wbcbench_split` | str | Which official split: `phase1_train` / `phase2_train` / `phase2_eval` / `phase2_test` |
| `severity` | str | `pristine` / `mild` / `moderate` / `extreme` (pristine config: always `pristine`) |
| `original_image_id` | str | For degraded rows: the pristine source `image_id`. Empty for pristine rows. |
| `patient_hash` | str | Truncated salted SHA-256 of the patient accession ID. Use for patient-level splits/grouping. |

## Quickstart

### Step 1: Install the Python packages (do this once)

```bash
pip install datasets huggingface_hub pandas pillow
```

> If you skip this step, the code below fails with `ModuleNotFoundError: No module named 'datasets'`.

### Step 2: Authenticate

After your access request is approved, log in once on your machine to cache a read-token:

```bash
huggingface-cli login   # paste a read-token from https://huggingface.co/settings/tokens
```

### Step 3: Load the data

```python
from datasets import load_dataset

REPO = "Xin-Tian/wbcbench2026"

# First call downloads parquet shards to ~/.cache/huggingface/. Subsequent calls reuse the cache.
# Full download: about 3.9 GB. You can also load just one split or stream rows (see below).
pristine_train = load_dataset(REPO, "pristine", split="phase2_train")
degraded_train = load_dataset(REPO, "degraded", split="phase2_train")

# Each row has: image (PIL.Image), image_id, label, wbcbench_split, severity,
# original_image_id, patient_hash. See the Schema section below.
row = pristine_train[0]
print(row["image_id"], row["label"], row["image"].size)
# -> "00004087" "SNE" (368, 370)
```

### Load only what you need (save disk / bandwidth)

```python
# Single split (about 500 MB for pristine, about 2 GB for degraded train):
train = load_dataset(REPO, "pristine", split="phase2_train")

# Stream rows without downloading the full shard:
ds = load_dataset(REPO, "degraded", split="phase2_train", streaming=True)
for row in ds.take(5):
    print(row["image_id"], row["label"])

# Slice notation (downloads only the requested shard):
sample = load_dataset(REPO, "pristine", split="phase2_train[:100]")
```

### Shard naming

Each split is split into parquet shards (about 500 MB each) named like `phase2_train-00000-of-00004.parquet`:

- `00000` = shard index (0-based, zero-padded)
- `of-00004` = total number of shards for this split

`load_dataset` finds them all automatically via the glob `phase2_train-*.parquet`. Degraded
splits have more shards than pristine ones because noisy/blurred JPEGs compress less efficiently
(about 85 KB/image vs about 20 KB/image), not because there are more rows.

### Linkage example: find the pristine source of a degraded image (fast)

For one-off lookups, `.filter()` on a 24K-row split takes about 13 seconds. Build a dict for O(1) lookups:

```python
# Build an in-memory index: image_id -> row index
pristine_idx = {iid: i for i, iid in enumerate(pristine_train["image_id"])}

# Pick any degraded image and find its pristine source
d = degraded_train[0]
p = pristine_train[pristine_idx[d["original_image_id"]]]

print(f"degraded {d['image_id']} ({d['severity']}) <- pristine {p['image_id']} ({p['label']})")
print(f"  degraded dims: {d['image'].size}  pristine dims: {p['image'].size}  (should be equal)")
```

### Load the class legend (abbreviation -> full cell type name)

```python
import pandas as pd
from huggingface_hub import hf_hub_download

legend = pd.read_csv(hf_hub_download(repo_id="Xin-Tian/wbcbench2026", filename="metadata/class_legend.csv", repo_type="dataset"))
LEGEND = dict(zip(legend["abbreviation"], legend["cell_type"]))
print(LEGEND["SNE"])  # -> "Segmented neutrophil"
```

## Patient-level separation

All splits are **patient-level disjoint**: every patient (identified by `patient_hash`) appears in
exactly one of `phase1_train`, `phase2_train`, `phase2_eval`, `phase2_test`. Verified across the
release: 493 unique patients, zero patients appear in more than one split. This is essential for
honest generalization benchmarks — no patient leakage between train and test.

Use `patient_hash` to group examples for cross-validation or to verify your own splits preserve
this property.

## Evaluation metric

The official WBCBench 2026 ranking metric is **macro-averaged F1 score** across all 13 classes
(equal weight per class, regardless of class frequency — important because the dataset is
severely class-imbalanced).

## Severity definitions

The degraded config applies one of four severity levels to each phase2 entry:

- **pristine** - no degradation applied (the image is identical to its pristine source bytes)
- **mild** - small Gaussian noise, low blur, mild color jitter
- **moderate** - moderate blur or motion blur + noticeable noise
- **extreme** - heavy motion blur, strong noise, strong color shift

Exact per-image parameters are in `metadata/degradation_params.csv`. The code that produced them is
in `scripts/degrade_ops.py`.

## Curation note

After the original Kaggle release, the WBCBench 2026 team commissioned a 10-team annotator review.
The expert reviewer's final decisions:

- **257 cells**: label corrected based on multi-annotator consensus
- **10 cells**: removed from the release (multi-cell artifacts or low-quality images)
- **74 cells**: reviewed but original label retained

The corrections are silently applied in the labels you see here. The original Kaggle CSV is
preserved upstream for reproducibility purposes.

## PII statement

All clinical PII (patient accession IDs, source paths, scan dates, machine identifiers) has been
removed. The `patient_hash` column is a truncated SHA-256 of (private salt + patient accession),
so users can group rows by patient without learning the patient's identity. The salt is not
published.

## Citation

```bibtex
@inproceedings{wbcbench2026,
  author = {Tian, Xin and Ma, Xudong and Yang, Tianqi and Achim, Alin and Papiez, Bartek and Watanaboonyongcharoen, Phandee and Anantrasirichai, Nantheera},
  title = {{WBCBench 2026}: A Challenge for Robust White Blood Cell Classification Under Class Imbalance},
  booktitle = {2026 IEEE International Symposium on Biomedical Imaging (ISBI)},
  year = {2026},
  publisher = {IEEE},
}
```