Attention Pooling Enhances NCA-based Classification of Microscopy Images

• URL: http://arxiv.org/abs/2508.12324v1
• Date: Sun, 17 Aug 2025 10:46:53 GMT
• Title: Attention Pooling Enhances NCA-based Classification of Microscopy Images
• Authors: Chen Yang, Michael Deutges, Jingsong Liu, Han Li, Nassir Navab, Carsten Marr, Ario Sadafi,
• Abstract summary: We integrate attention pooling with Neural Cellular Automata to enhance feature extraction and improve classification accuracy.<n>We evaluate our method on eight diverse microscopy image datasets and demonstrate that our approach significantly outperforms existing NCA methods.<n>Our results highlight the potential of NCA-based models an alternative for explainable image classification.
• Score: 45.60974312463409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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