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wbcbench2026

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Image Classification
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medical
hematology
white-blood-cell
wbc
robustness
class-imbalance
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metadata 
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. A 13-class white-blood-cell classification benchmark with mixed pristine and degraded images for robustness evaluation under realistic imaging conditions.

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) 

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:

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

Step 3: Load the data 

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) 

# 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:

# 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) 

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 

@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},
}