Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images

arXiv:2404.05584v1 Announce Type: new
Abstract: 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 …

abstract arxiv cells cellular classification cs.cv deep learning deep learning techniques diagnosis eess.iv however identification images process robust scale solution type