Diagnosis of hematological malignancies depends on accurate identification of white blood cells in peripheral blood smears. Deep learning techniques are emerging as a viable solution to scale and optimize this process by automatic identification of cells in laboratories. However, these techniques face several challenges such as limited generalizability, sensitivity to domain shifts and lack of explainability. Here, we are introducing a novel approach based on neural cellular automata (NCA) for white blood cell classification. We test our approach on three datasets of white blood cell images and show that we achieve competitive performance compared to conventional methods. Our NCA-based method is significantly smaller in terms of parameters and exhibits robustness to domain shifts. Furthermore, the architecture is inherently explainable, providing insights into the decision process for each classification, helping experts understand and validate model predictions. Results demonstrate that NCA not only can be used for image classification, but also address key challenges of conventional methods, indicating a high potential for applicability in clinical practice.

翻译：血液系统恶性肿瘤的诊断依赖于外周血涂片中白细胞的准确识别。深度学习技术正逐渐成为实验室自动化细胞识别过程中规模化与优化这一流程的有效方案。然而，这些技术面临泛化能力有限、对域迁移敏感以及缺乏可解释性等挑战。本文提出一种基于神经细胞自动机（NCA）的白细胞分类新方法。我们在三个白细胞图像数据集上验证了该方法，结果表明其性能与传统方法相比具有竞争力。我们提出的基于NCA的方法在参数量上显著更小，且对域迁移表现出鲁棒性。此外，该架构具有内在的可解释性，能为每次分类的决策过程提供洞察，有助于专家理解并验证模型预测结果。实验结果表明，NCA不仅能用于图像分类，还能解决传统方法的关键挑战，展现出在临床实践中应用的巨大潜力。