Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images
This work addresses challenges in medical imaging for clinical practice by improving generalizability, robustness, and explainability in white blood cell classification.
The paper tackles the problem of classifying white blood cell images for diagnosing hematological malignancies by introducing a neural cellular automata (NCA) approach, achieving competitive performance with significantly fewer parameters and robustness to domain shifts.
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.