Towards Practical Alzheimer's Disease Diagnosis: A Lightweight and Interpretable Spiking Neural Model
Journal:
arXiv
Published Date:
Jun 11, 2025
Abstract
Early diagnosis of Alzheimer's Disease (AD), especially at the mild cognitive
impairment (MCI) stage, is vital yet hindered by subjective assessments and the
high cost of multimodal imaging modalities. Although deep learning methods
offer automated alternatives, their energy inefficiency and computational
demands limit real-world deployment, particularly in resource-constrained
settings. As a brain-inspired paradigm, spiking neural networks (SNNs) are
inherently well-suited for modeling the sparse, event-driven patterns of neural
degeneration in AD, offering a promising foundation for interpretable and
low-power medical diagnostics. However, existing SNNs often suffer from weak
expressiveness and unstable training, which restrict their effectiveness in
complex medical tasks. To address these limitations, we propose FasterSNN, a
hybrid neural architecture that integrates biologically inspired LIF neurons
with region-adaptive convolution and multi-scale spiking attention. This design
enables sparse, efficient processing of 3D MRI while preserving diagnostic
accuracy. Experiments on benchmark datasets demonstrate that FasterSNN achieves
competitive performance with substantially improved efficiency and stability,
supporting its potential for practical AD screening. Our source code is
available at https://github.com/wuchangw/FasterSNN.
