Neural Cellular Automata for Lightweight, Robust and Explainable
  Classification of White Blood Cell Images
Michael Deutges Ario Sadafi Nassir Navab Carsten Marr
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.