针对染色差异导致的白细胞分类失效问题，提出分层集成推理方案。
A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts
- 构建三级分层集成架构，结合kNN检索与特征库增强鲁棒性
- 在WBCBench数据集上宏F1-score进入前十，精准识别罕见原始细胞
- 适合临床场景中跨实验室、多设备部署的白血病筛查系统
自动化白细胞（WBC）分类对大规模白血病筛查至关重要。然而，真实场景中的染色协议差异、扫描仪特性及实验室间变异引发的领域偏移，常导致模型性能下降。2026年ISBI举办的白细胞分类挑战赛（WBCBench）旨在推动鲁棒性白细胞识别，重点在于准确识别原始细胞及其他临床关键稀有亚型。本文提出一种基于特征库与DinoBloom主干网络（经LoRA微调）的记忆增强型分层集成推理管道。该三阶段推理结构在每级均采用k近邻（kNN）检索，降低对单一决策的依赖。在WBCBench数据集上的评估显示，本方法在最终测试阶段宏F1-score排名进入前十大。
原文摘要 · Abstract (English)
Automated white blood cell (WBC) classification is essential for scalable leukaemia screening. However, real-world deployment is challenged by domain shifts caused by staining protocols, scanner characteristics, and inter-laboratory variability, which often degrade model performance. The White Blood Cell Classification Challenge (WBCBench) at ISBI 2026 aims to advance robust WBC recognition, with a focus on accurately identifying blast cells and other clinically critical rare subtypes. We propose a memory-augmented, hierarchical ensemble pipeline for WBC classification under domain shifts, leveraging a feature bank and a DinoBloom backbone fine-tuned with LoRA. Our three-stage inference hierarchy combines k-nearest neighbors (kNN) retrieval at each level, reducing over-reliance on any single decision. Evaluated on the WBCBench dataset, our method ranks within the top ten by macro F1-score in the final testing phase.
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