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 cell classification. However, these techniques face several challenges such as limited generalizability, sensitivity to domain shifts, and lack of explainability. Here, we introduce a novel approach for white blood cell classification based on neural cellular automata (NCA). 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, which helps to understand and validate model predictions. Our results demonstrate that NCA can be used for image classification, and that they 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对外周血涂片中白细胞的准确识别。深度学习技术正通过自动细胞分类成为扩展和优化这一过程的可行解决方案。然而，这些技术面临着若干挑战，例如泛化能力有限、对领域偏移敏感以及缺乏可解释性。本文提出了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测试了该方法，结果表明，与传统方法相比，我们的方法取得了具有竞争力的性能。我们基于NCA的方法在参数量上显著更小，并且对领域偏移表现出鲁棒性。此外，该架构本身具有可解释性，能够为每次分类提供决策过程的深入见解，这有助于理解和验证模型的预测。我们的研究结果证明，NCA可用于图像分类，并且能够解决传统方法的关键挑战，表明其在临床实践中具有很高的应用潜力。