WBCBench 2026: Robust White Blood Cell Classification Challenge Session @ ISBI 2026, London, UK

A benchmark challenge for automated white blood cell (WBC) classification under severe class imbalance, fine-grained morphology, and simulated domain shift. Hosted as a private Kaggle competition with standardized evaluation for fair, reproducible comparison.

🏆🏆🏆🏆 Private Kaggle Competition Macro-F1 Primary Metric Patient-level Separation Open Evaluator

Introduction

Leukaemia is a group of cancers that originate in the bone marrow and lead to an overproduction of abnormal white blood cells. Diagnosis often relies on microscopic examination (with flow cytometry or genetic testing for confirmation), which can be resource-intensive and time-consuming. Automating WBC classification offers a promising alternative, especially for under-resourced laboratories, yet robust detection of blast and rare cells in blood smear images requires further validation and optimization.

This challenge evaluates algorithms on single-site images with standardized staining and acquisition. To replicate real-world domain shift, we synthesize scanner and settings variability (e.g., noise and blur). The held-out test set enforces patient-level separation; train/validation use group-stratified splits to preserve minority coverage.

Key Facts

  • 13 WBC classes; expert hematopathologist annotations
  • Severe class imbalance & fine-grained morphology
  • Standardized submission schema & open evaluator
  • Main metric: macro-averaged F1

Goals & Objectives

G1 — Comparability & Reproducibility

Standardized splits and an open evaluator with a fixed submission format.

G2 — Rare-Class Reliability

Prioritize macro-F1 and require class-wise reporting to highlight minority performance.

G3 — Practical Impact

Provide baselines and a clear submission workflow; publish a concise post-challenge summary and recommended practices.

Important Dates

  • November 19 — Training Dataset Phase 1 (15% of patients) Released
    phase1_train – 74 patients, 8,288 images. This clean, well-curated subset is available on Kaggle for download and serves as the initial training set and sanity-check benchmark for participants.
  • November 26 — Training Dataset Phase 2 (remaining 85% of patients) Released with Evaluation Set
    phase2_train (45%) – 222 patients, 24,897 images
    phase2_eval (10%) – 49 patients, 5,350 images
    phase2_test (30%) – 148 patients, 16,477 images (not released; used internally for leaderboard ranking)
    Teams can begin submitting results for leaderboard evaluation from this point.
  • February 6 — Early Code + Weights Submission Deadline (Top 10 Teams)
    For reproducibility checking, we will contact teams provisionally ranked in the top 10 by email three days prior to this deadline and request them to submit their code and model weights.
  • February 26 — Paper Submission Deadline (4 pages paper in IEEE ISBI format, EDAS, Challenge Track) & Competition Closes
    Submit your challenge paper on the ISBI 2026 EDAS platform under the Challenge Track. On this date, the Kaggle submission portal closes and no new submissions are accepted. We will validate the submitted code/models to confirm the final leaderboard.
  • March 1 — Reviews Released on EDAS
    Authors receive reviews and have two weeks to address comments and make minor updates to the manuscript or method description.
  • March 15 — Camera-ready Deadline & Top-performing Teams Announced
    Upload the final paper on EDAS. Meanwhile, the preliminary top-ranking teams will be announced on our website.
  • March 20 — Presentation Format Notification
    Authors are informed whether their presentation is oral or poster.
  • April 8–11 — Winners Announced
    Final results and award announcements during ISBI 2026.

Final dates follow the official ISBI 2026 schedule. Please refer to the ISBI Challenge page for any updates: https://biomedicalimaging.org/2026/challenges/

Team Eligibility

  • Open to academia, industry, and independent teams (subject to local laws/sanctions).
  • One registered team per method entry; overlapping membership must be declared.
  • Organizers' institutes may participate and appear on the leaderboard, but are ineligible for prizes.
  • External public data/pretrained models allowed with disclosure; private/undisclosed data disallowed.
  • Ethical use only: no attempts to re-identify subjects or circumvent de-identification.

Registration & Access

Participation is by registration and approval. As a private Kaggle competition, access to the dataset and leaderboard submissions will be granted after you register below and your information is approved by the organizers.

  • Please ensure you have a valid Kaggle account.
  • Provide your Kaggle username and the email linked to your Kaggle account as your primary contact email in the registration form.
  • Once your registration is approved, we will email you the competition access link. Please make sure you use the same email to access Kaggle so that the invitation can be applied correctly.

Registration (Closed) Our challenge on Kaggle

Dataset

The dataset comprises 55,012 peripheral blood smear images across 13 WBC classes. Images are provided as 368×368 px JPEG files and were annotated by expert hematopathologists. The dataset is highly imbalanced, reflecting real-world prevalence: common classes (e.g., segmented neutrophils) dominate, while rare leukocytes appear only a few times.

The dataset will be made available via a dedicated Kaggle link at launch (training/validation downloadable; test set hidden for fair evaluation). Licensing will follow an appropriate open research license (e.g., CC BY-NC-SA 4.0) to encourage non-commercial research and reproducibility.

Figure 1 shows representative examples of the 13 WBC categories.

Figure 1: Example images of the 13 white blood cell classes
Figure 1. Example images of the 13 white blood cell classes. (A) Segmented Neutrophil, (B) Lymphocyte, (C) Monocyte, (D) Blast cell, (E) Eosinophil, (F) Band form neutrophil, (G) Myelocyte, (H) Metamyelocyte, (I) Variant lymphocyte, (J) Basophil, (K) Promyelocyte, (L) Plasma cell, (M) Prolymphocyte.

Evaluation

Ranking Rules

  • Primary metric: macro-averaged F1 across all classes.
  • If tied, use balanced accuracy; if still tied, use macro-averaged precision.
  • If still tied, use macro-averaged specificity.
  • If all above tie, final ranking will be determined by inference time (verified by organizers).

Competition Rules

  • On Kaggle, each team is allowed a maximum of 10 submissions per day for leaderboard evaluation.
  • Top 5 teams will be asked to submit executable code and trained models for verification.

A fixed submission schema and open evaluator are provided to ensure fair, reproducible benchmarking.

Awards & Prizes

  • 1st: 🏆🏆🏆   2nd: 🏆🏆   3rd: 🏆   (Prize amounts to be announced later)
  • Top five teams invited to present at ISBI 2026; winners announced at the venue.

Prize Eligibility

  • Submit executable code and trained models for verification.
  • Submit a quality paper describing methods and training strategy.
  • At least one team member presents in person at ISBI.

Organizers

  • Nantheera Anantrasirichai, University of Bristol, UK
  • Alin Achim, University of Bristol, UK
  • Bartek Papiez, University of Oxford, UK
  • Xudong Ma, University of Oxford, UK
  • Xin Tian, University of Oxford, UK
  • Tianqi Yang, University College London, UK
  • Phandee Watanaboonyongcharoen, Chulalongkorn University, Thailand

Annotation team (Laboratory Medicine, Chulalongkorn University): Phandee Watanaboonyongcharoen (Lead), Kwanlada Chaiwong, Manissara Yeekaday, Rujira Naksith, Suppakorn Wongkamchai, Jirapa Kaewkhruawan, Luksamon Tipsuriya, Wararat Masalae, Waroonkarn Laiklang, Pitchayaporn Riyagoon, Sunudda Nowaratsopon, Tapakorn Thepnarin, Winyanan Nunphuak.

Contact

Organizers & Partners

University of Bristol logo
University of Oxford logo
University College London logo
Chulalongkorn University logo
ISBI logo
IEEE logo