Recognizing the types of white blood cells (WBCs) in microscopic images of human blood smears is a fundamental task in the fields of pathology and hematology. Although previous studies have made significant contributions to the development of methods and datasets, few papers have investigated benchmarks or baselines that others can easily refer to. For instance, we observed notable variations in the reported accuracies of the same Convolutional Neural Network (CNN) model across different studies, yet no public implementation exists to reproduce these results. In this paper, we establish a benchmark for WBC recognition. Our results indicate that CNN-based models achieve high accuracy when trained and tested under similar imaging conditions. However, their performance drops significantly when tested under different conditions. Moreover, the ResNet classifier, which has been widely employed in previous work, exhibits an unreasonably poor generalization ability under domain shifts due to batch normalization. We investigate this issue and suggest some alternative normalization techniques that can mitigate it. We make fully-reproducible code publicly available\footnote{\url{https://github.com/apple2373/wbc-benchmark}}.

翻译：在人血液涂片显微图像中识别白细胞类型是病理学与血液学领域的基础任务。尽管先前的研究在方法与数据集开发方面做出了重要贡献，但少有论文探讨可供他人便捷参考的基准或基线。例如，我们观察到同一卷积神经网络模型在不同研究报告中的准确率存在显著差异，却缺乏公开实现来复现这些结果。本文建立了白细胞识别的基准。结果表明，在相似成像条件下训练和测试时，基于CNN的模型能达到高准确率；然而，在不同条件下测试时其性能显著下降。此外，先前工作广泛使用的ResNet分类器由于批量归一化在域偏移下表现出不合理地弱泛化能力。我们对此问题进行了研究，并提出了若干可缓解该问题的替代归一化技术。我们公开了完全可复现的代码\footnote{\url{https://github.com/apple2373/wbc-benchmark}}。