# Attention Pooling Enhances NCA-based Classification of Microscopy Images Source: [Attention Pooling Enhances NCA-based Classification of Microscopy Images]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:24:53.663Z Authors: Chen Yang, Michael Deutges, Jingsong Liu, Han Li, Nassir Navab, Carsten Marr, Ario Sadafi Published: 2025-08-17T10:46:53.000Z ## Abstract Neural Cellular Automata (NCA) offer a robust and interpretable approach to image classification, making them a promising choice for microscopy image analysis. However, a performance gap remains between NCA and larger, more complex architectures. We address this challenge by integrating attention pooling with NCA to enhance feature extraction and improve classification accuracy. The attention pooling mechanism refines the focus on the most informative regions, leading to more accurate predictions. We evaluate our method on eight diverse microscopy image datasets and demonstrate that our approach significantly outperforms existing NCA methods while remaining parameter-efficient and explainable. Furthermore, we compare our method with traditional lightweight convolutional neural network and vision transformer architectures, showing improved performance while maintaining a significantly lower parameter count. Our results highlight the potential of NCA-based models an alternative for explainable image classification. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()