用轻量级模型集成提升罕见血细胞分类准确率
Ensemble of Small Classifiers For Imbalanced White Blood Cell Classification
- 采用3种轻量预训练模型的集成策略，通过分层三折交叉验证训练
- 在不平衡数据集上实现优异性能，对粒细胞和淋巴细胞分类效果显著
- 适合血液病诊断研究者与医学图像算法开发者参考
自动化白血病诊断中的白血细胞分类可替代耗时且资源密集的病理学家显微检查。然而，由于染色、扫描差异及患者间异质性，稀有细胞类型的分类仍具挑战。本文提出一种针对粒细胞生成、单核细胞生成和淋巴细胞生成生物学过程的轻量级集成方法。通过数据集扩展缓解类别不平衡问题，实验表明，简单集成SwinV2-Tiny、DinoBloom-Small和ConvNeXT-V2-Tiny三种轻量预训练模型，在该挑战性数据集上表现优异。每种架构训练3个实例，采用分层三折交叉验证；输入图像经9个模型前向传播后，通过逻辑值平均聚合结果。此外，分析了模型在区分形态相似的早幼粒细胞与淋巴细胞时的局限性。代码已开源：https://gitlab.com/siddharthsrivastava/wbc-bench-2026。
原文摘要 · Abstract (English)
Automating white blood cell classification for diagnosis of leukaemia is a promising alternative to time-consuming and resource-intensive examination of cells by expert pathologists. However, designing robust algorithms for classification of rare cell types remains challenging due to variations in staining, scanning and inter-patient heterogeneity. We propose a lightweight ensemble approach for classification of cells during Haematopoiesis, with a focus on the biology of Granulopoiesis, Monocytopoiesis and Lymphopoiesis. Through dataset expansion to alleviate some class imbalance, we demonstrate that a simple ensemble of lightweight pretrained SwinV2-Tiny, DinoBloom-Small and ConvNeXT-V2-Tiny models achieves excellent performance on this challenging dataset. We train 3 instantiations of each architecture in a stratified 3-fold cross-validation framework; for an input image, we forward-pass through all 9 models and aggregate through logit averaging. We further reason on the weaknesses of our model in confusing similar-looking myelocytes in granulopoiesis and lymphocytes in lymphopoiesis. Code: https://gitlab.com/siddharthsrivastava/wbc-bench-2026.
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