用定制脉冲神经网络提升肝脏疾病诊断准确率，达98.35%。
Improving Liver Disease Diagnosis with SNNDeep: A Custom Spiking Neural Network Using Diverse Learning Algorithms
- 自研脉冲神经网络SNNDeep，结合三种学习算法优化临床诊断性能。
- 在肝病CT图像分类中达到98.35%最高验证准确率，优于现有框架。
- 适合数据少、时间紧的医疗诊断场景，推动神经启发AI落地精准医疗。
脉冲神经网络（SNN）作为节能且符合生物机制的深度学习替代方案，其在高风险医学影像中的应用几乎未被探索。本研究提出SNNDeep，首个专为从计算机断层扫描（CT）特征中二分类肝脏健康状态而设计的定制化脉冲神经网络。为确保临床相关性与广泛泛化能力，模型基于医学分割挑战赛（Medical Segmentation Decathlon）中的Task03\Liver数据集进行开发与评估。对比了三种根本不同的学习算法：代理梯度学习、Tempotron规则与生物启发式主动学习，并测试了三种架构变体：完全自定义的底层模型，以及基于领先SNN框架snnTorch和SpikingJelly的实现。通过Optuna进行超参数优化。结果表明，自研的SNNDeep持续优于框架实现，最高验证准确率达98.35%，对不同学习规则具有更强适应性，且训练开销显著降低。本研究首次提供实证证据，证明低层次、高度可调的SNN可在医学影像领域超越标准框架，尤其适用于数据有限、时间受限的诊断场景，为神经启发人工智能在精准医疗中的应用开辟新路径。
原文摘要 · Abstract (English)
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.
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