用注意力池化提升显微图像分类的NCA模型性能
Attention Pooling Enhances NCA-based Classification of Microscopy Images
- 在NCA中引入注意力池化，聚焦关键区域特征
- 8个数据集上显著优于现有NCA方法，参数量更低
- 适合需要可解释性的生物医学图像分析场景
神经元胞自动机（NCA）为图像分类提供了稳健且可解释的方法，适用于显微图像分析。然而，其性能仍落后于更大更复杂的架构。本文通过将注意力池化机制融入NCA，增强特征提取能力，提升分类准确率。该机制能精准聚焦最具信息量的图像区域，从而提高预测精度。我们在八个多样化的显微图像数据集上评估该方法，结果表明其显著优于现有NCA模型，同时保持参数效率和可解释性。此外，与传统轻量级卷积神经网络及视觉变压器相比，本方法在性能上表现更优，参数量却显著更低。研究凸显了基于NCA模型在可解释图像分类中的潜力。
原文摘要 · Abstract (English)
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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