用复杂度分析提升脉冲神经网络，实现高精度乳腺癌检测。
Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection
- 结合莱普尔-茨维复杂度分析脉冲活动模式，增强模型可解释性。
- 脉冲神经网络在乳腺癌数据集上最高达98.25%准确率，超传统模型。
- 计算成本仅为传统方法1/100，适合实时或资源受限系统。
脉冲神经网络（SNN）的事件驱动特性使其能高效编码时空特征，适用于动态时序数据处理。尽管具有生物学合理性，但因难以达到传统深度学习模型的性能，其在医学图像识别中应用有限。为此，本文提出一种新型乳腺癌分类方法，将SNN与莱普尔-茨维复杂度（LZC）结合，该方法通过捕捉神经活动中的结构模式，提升脉冲模型的可解释性和准确性。研究对比了生物物理型漏电积分-发放（LIF）和概率型列维-巴克斯特（LB）神经元模型，在监督、无监督及混合学习范式下进行实验，使用来自医学影像的数值特征对威斯康星乳腺癌数据集进行测试。基于LB的模型准确率持续超过90.00%，而基于LIF的模型则超过85.00%。通过将人工神经网络转为脉冲神经网络的方法，两种模型均达到最高98.25%的准确率，媲美传统反向传播深度学习模型，但计算成本最多降低至1/100。该混合方法融合深度学习性能与脉冲网络的效率与生物合理性，实现了高性能低开销的诊断方案。我们推测，时间编码、脉冲稀疏性与LZC驱动的复杂性分析协同作用，提升了特征提取效率。研究结果表明，结合LZC的SNN为医疗诊断提供了有前景的生物合理替代方案，尤其适用于资源受限或实时系统。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) event-driven nature enables efficient encoding of spatial and temporal features, making them suitable for dynamic time-dependent data processing. Despite their biological relevance, SNNs have seen limited application in medical image recognition due to difficulties in matching the performance of conventional deep learning models. To address this, we propose a novel breast cancer classification approach that combines SNNs with Lempel-Ziv Complexity (LZC) a computationally efficient measure of sequence complexity. LZC enhances the interpretability and accuracy of spike-based models by capturing structural patterns in neural activity. Our study explores both biophysical Leaky Integrate-and-Fire (LIF) and probabilistic Levy-Baxter (LB) neuron models under supervised, unsupervised, and hybrid learning regimes. Experiments were conducted on the Breast Cancer Wisconsin dataset using numerical features derived from medical imaging. LB-based models consistently exceeded 90.00% accuracy, while LIF-based models reached over 85.00%. The highest accuracy of 98.25% was achieved using an ANN-to-SNN conversion method applied to both neuron models comparable to traditional deep learning with back-propagation, but at up to 100 times lower computational cost. This hybrid approach merges deep learning performance with the efficiency and plausibility of SNNs, yielding top results at lower computational cost. We hypothesize that the synergy between temporal-coding, spike-sparsity, and LZC-driven complexity analysis enables more-efficient feature extraction. Our findings demonstrate that SNNs combined with LZC offer promising, biologically plausible alternative to conventional neural networks in medical diagnostics, particularly for resource-constrained or real-time systems.
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