SCINTILLA-SNN: A Spiking Multi-Scale Selective Aggregation Network for Perineural Invasion Prediction

2026-09-10

AI Summary

为解决胆管癌术前神经周侵犯预测难题，提出SCINTILLA-SNN，一种3D脉冲网络，通过多尺度选择性聚合稀疏证据，提高了预测准确性并降低了能耗。

Abstract

Preoperative prediction of perineural invasion (PNI) in cholangiocarcinoma (CCA) is clinically valuable but remains challenging because PNI-related cues on magnetic resonance imaging (MRI) are subtle, sparse, and spatially localized around the tumor boundary. Standard 3D CNN and transformer architectures process volumetric data in a dense or spatially uniform manner, which can dilute subtle PNI-related evidence while requiring a large number of multiply-accumulate operations over 3D feature grids. To address these limitations, we propose SCINTILLA-SNN, a 3D spiking network composed of a four-stage hierarchical backbone and a Multi-Scale Spike Aggregation (MSSA) module for PNI prediction. The backbone extracts hierarchical volumetric representations through spiking convolutional stages and local spike window modulation stages. Given the resulting stage-wise representations, MSSA maps each spatial token to a learnable content value and modulates it with a spike-dynamics gate derived from firing rate and timestep-wise membrane-potential variability. The resulting score, referred to as the diagnostic token score, is used to selectively aggregate sparse PNI-related evidence. Experiments on a 10-year retrospective cohort of 182 CCA patients show that SCINTILLA-SNN achieves an AUROC of 0.748 under 5-fold cross-validation, while reducing the estimated inference energy by 23.18$\times$ compared with dense MAC-only computation of the same network.

Problem

• Perineural Invasion
• Cholangiocarcinoma
• Magnetic Resonance Imaging
• 3D CNN
• Transformer

Innovation

• Spiking Neural Network
• Multi-Scale Spike Aggregation
• Perineural Invasion Prediction
• Energy Efficiency