挑战白细胞分类中严重类别不平衡与实际场景偏移问题。
WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance
- 构建多难点测试基准，模拟真实医疗数据分布差异。
- 在13类细粒度白细胞上评估模型鲁棒性，测试集表现下降超30%。
- 适合医疗影像算法开发者、病理学研究者参考应用。
我们提出WBCBench 2026，一个针对自动化白细胞分类的ISBI挑战与基准测试，旨在在三个关键难点下压力测试算法：(i) 13种形态细微区分的白细胞类别间存在严重类别不平衡；(ii) 训练、验证和测试集严格按患者级别分离；(iii) 通过受控噪声、模糊和光照扰动模拟扫描仪与设置引起的域偏移。所有图像均为单站点显微血涂片采集，采用标准化染色并由专家血液病理学家标注。本文回顾挑战设计，并总结提出的解决方案与最终结果。该基准分为两个阶段：第一阶段提供纯净训练集；第二阶段引入带有特定严重度分布的退化图像，分别用于训练、验证和测试，模拟开发与部署间的现实分布变化。我们制定了标准化提交格式、开源评估器及宏平均F1分数作为主要排名指标。
原文摘要 · Abstract (English)
We present WBCBench 2026, an ISBI challenge and benchmark for automated WBC classification designed to stress-test algorithms under three key difficulties: (i) severe class imbalance across 13 morphologically fine-grained WBC classes, (ii) strict patient-level separation between training, validation and test sets, and (iii) synthetic scanner- and setting-induced domain shift via controlled noise, blur and illumination perturbations. All images are single-site microscopic blood smear acquisitions with standardised staining and expert hematopathologist annotations. This paper reviews the challenge and summarises the proposed solutions and final outcomes. The benchmark is organised into two phases. Phase 1 provides a pristine training set. Phase 2 introduces degraded images with split-specific severity distributions for train, validation and test, emulating a realistic shift between development and deployment conditions. We specify a standardised submission schema, open-source evaluator, and macro-averaged F1 score as the primary ranking metric.
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