White blood cells (WBCs) play a crucial role in safeguarding the human body against pathogens and foreign substances. Leveraging the abundance of WBC imaging data and the power of deep learning algorithms, automated WBC analysis has the potential for remarkable accuracy. However, the capability of deep learning models to explain their WBC classification remains largely unexplored. In this study, we introduce HemaX, an explainable deep neural network-based model that produces pathologist-like explanations using five attributes: granularity, cytoplasm color, nucleus shape, size relative to red blood cells, and nucleus to cytoplasm ratio (N:C), along with cell classification, localization, and segmentation. HemaX is trained and evaluated on a novel dataset, LeukoX, comprising 467 blood smear images encompassing ten (10) WBC types. The proposed model achieves impressive results, with an average classification accuracy of 81.08% and a Jaccard index of 89.16% for cell localization. Additionally, HemaX performs well in generating the five explanations with a normalized mean square error of 0.0317 for N:C ratio and over 80% accuracy for the other four attributes. Comprehensive experiments comparing against multiple state-of-the-art models demonstrate that HemaX's classification accuracy remains unaffected by its ability to provide explanations. Moreover, empirical analyses and validation by expert hematologists confirm the faithfulness of explanations predicted by our proposed model.

翻译：白细胞在保护人体免受病原体和外来物质侵害中发挥关键作用。借助丰富的白细胞成像数据和深度学习算法的强大能力，自动化白细胞分析有望实现卓越的准确率。然而，深度学习模型对其白细胞分类结果的解释能力仍鲜有探究。本研究提出HemaX——一种基于可解释深度神经网络模型，该模型通过五个属性（颗粒度、细胞质颜色、细胞核形态、相对于红细胞的尺寸及核质比）生成病理学家式解释，同时实现细胞分类、定位与分割。HemaX在包含十种白细胞类型的467张血涂片图像新数据集LeukoX上进行训练与评估。所提模型取得了显著成果：平均分类准确率达81.08%，细胞定位Jaccard指数为89.16%。此外，HemaX在生成五种解释时表现优异：核质比的归一化均方误差为0.0317，其余四个属性的准确率均超80%。通过多项与前沿模型的对比实验证明，HemaX的分类准确率不受其解释能力的影响。同时，经验性分析与资深血液学专家的验证确认了模型所预测解释的可靠性。