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Towards Practical Alzheimer's Disease Diagnosis: A Lightweight and Interpretable Spiking Neural Model

Created by
  • Haebom

Author

Changwei Wu, Yifei Chen, Yuxin Du, Jinying Zong, Jie Dong, Mingxuan Liu, Yong Peng, Jin Fan, Feiwei Qin, Changmiao Wang

Outline

In this paper, we propose a novel energy-efficient and interpretable spiking neural network (SNN)-based architecture, FasterSNN, for early diagnosis of Alzheimer's disease (AD), especially in the mild cognitive impairment (MCI) stage. To overcome the weak representational ability and unstable training problems of conventional SNNs, we design a hybrid neural network architecture that integrates biologically inspired LIF neurons, locally adaptive convolutions, and multi-scale spiking attention. It efficiently processes 3D MRI data while maintaining diagnostic accuracy, and demonstrates competitive performance, improved efficiency, and stability over conventional methods through experiments on benchmark datasets. The source code is open source.

Takeaways, Limitations

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Takeaways:
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Presenting the possibility of building an energy-efficient SNN-based early diagnosis system for Alzheimer's disease.
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Improvement of low expressiveness and unstable learning problems of existing SNNs (__T25232_____).
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Efficient 3D MRI processing using locally adaptive convolution and multi-scale spiking attention.
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Improving the reliability of medical diagnosis through designing highly interpretable models.
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Presentation of a lightweight model with high applicability to real environments.
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Limitations:
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A more detailed analysis is needed to determine how well the proposed model performs compared to other state-of-the-art models.
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Need to evaluate generalization performance on various datasets.
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Validation in real clinical settings is needed.
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Lack of quantitative assessment of the interpretability of SNNs.
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