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

arXiv: https://arxiv.org/abs/2404.05584 · HTML: https://mlsanity.com/paper/2404.05584/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Attention-based Neural Cellular Automata](https://mlsanity.com/paper/2211.01233/) (2022)
- [Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata](https://mlsanity.com/paper/2302.03473/) (2023)
- [Growing Isotropic Neural Cellular Automata](https://mlsanity.com/paper/2205.01681/) (2022)
- [Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification](https://mlsanity.com/paper/2207.00501/) (2022)
- [A Continual Learning Approach for Cross-Domain White Blood Cell Classification](https://mlsanity.com/paper/2308.12679/) (2023)
- [Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images](https://mlsanity.com/paper/2303.08632/) (2023)
- [A cellular automata approach to local patterns for texture recognition](https://mlsanity.com/paper/2007.07462/) (2020)

## Built on by (most cited first, 4 of 4)

- [Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells](https://mlsanity.com/paper/2508.12322/) (2025)
- [Attention Pooling Enhances NCA-based Classification of Microscopy Images](https://mlsanity.com/paper/2508.12324/) (2025)
- [Measuring Prediction Uncertainty in Neural Cellular Automata](https://mlsanity.com/paper/2605.26726/) (2026)
- [Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunities](https://mlsanity.com/paper/2609.24595/) (2026)

## Abstract (arXiv)

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.