Purpose: Spiking neural networks (SNNs) have recently gained attention as energy-efficient, biologically plausible alternatives to conventional deep learning models. Their application in high-stakes biomedical imaging remains almost entirely unexplored. Methods: This study introduces SNNDeep, the first tailored SNN specifically optimized for binary classification of liver health status from computed tomography (CT) features. To ensure clinical relevance and broad generalizability, the model was developed and evaluated using the Task03\Liver dataset from the Medical Segmentation Decathlon (MSD), a standardized benchmark widely used for assessing performance across diverse medical imaging tasks. We benchmark three fundamentally different learning algorithms, namely Surrogate Gradient Learning, the Tempotron rule, and Bio-Inspired Active Learning across three architectural variants: a fully customized low-level model built from scratch, and two implementations using leading SNN frameworks, i.e., snnTorch and SpikingJelly. Hyperparameter optimization was performed using Optuna. Results: Our results demonstrate that the custom-built SNNDeep consistently outperforms framework-based implementations, achieving a maximum validation accuracy of 98.35%, superior adaptability across learning rules, and significantly reduced training overhead. Conclusion:This study provides the first empirical evidence that low-level, highly tunable SNNs can surpass standard frameworks in medical imaging, especially in data-limited, temporally constrained diagnostic settings, thereby opening a new pathway for neuro-inspired AI in precision medicine.

翻译：目的：脉冲神经网络（SNNs）作为传统深度学习模型的节能、生物可解释替代方案，近期受到广泛关注，但其在高风险生物医学成像中的应用几乎尚未被探索。方法：本研究提出SNNDeep——首个针对计算机断层扫描（CT）特征进行肝脏健康状态二分类优化而专门设计的SNN模型。为确保临床相关性和广泛泛化性，模型使用Medical Segmentation Decathlon（MSD）标准化基准中的Task03肝脏数据集进行开发和评估，该数据集广泛用于评估不同医学成像任务的性能。我们对比了三种根本不同的学习算法（代理梯度学习、Tempotron规则和生物启发主动学习）在三种架构变体中的表现：完全自底向上构建的定制低层模型，以及基于领先SNN框架（snnTorch和SpikingJelly）的两种实现。使用Optuna进行超参数优化。结果：实验结果表明，定制构建的SNNDeep持续优于基于框架的实现，达到98.35%的最高验证准确率，具有跨学习规则的卓越适应性，并显著降低训练开销。结论：本研究首次提供经验证据，证明低层、高度可调谐的SNN在医学成像中（尤其在数据有限、时间受限的诊断场景中）可超越标准框架，为神经启发式AI在精准医学中的应用开辟了新路径。