Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware. Significant advancements have been made in SNN-based convolutional neural networks (CNNs) and Transformer architectures. However, neuromorphic computing for the medical imaging domain remains underexplored. In this study, we introduce EG-SpikeFormer, an SNN architecture tailored for clinical tasks that incorporates eye-gaze data to guide the model's attention to the diagnostically relevant regions in medical images. Our developed approach effectively addresses shortcut learning issues commonly observed in conventional models, especially in scenarios with limited clinical data and high demands for model reliability, generalizability, and transparency. Our EG-SpikeFormer not only demonstrates superior energy efficiency and performance in medical image prediction tasks but also enhances clinical relevance through multi-modal information alignment. By incorporating eye-gaze data, the model improves interpretability and generalization, opening new directions for applying neuromorphic computing in healthcare.

翻译：神经形态计算已成为传统人工智能的一种有前景的节能替代方案，其主要利用在神经形态硬件上实现的脉冲神经网络。基于脉冲神经网络的卷积神经网络和Transformer架构已取得显著进展。然而，针对医学成像领域的神经形态计算研究仍显不足。在本研究中，我们提出了EG-SpikeFormer，这是一种专为临床任务设计的SNN架构，它整合了眼动数据以引导模型关注医学图像中具有诊断相关性的区域。我们开发的方法有效解决了传统模型中常见的捷径学习问题，尤其在临床数据有限且对模型可靠性、泛化能力和透明度要求较高的场景中。我们的EG-SpikeFormer不仅在医学图像预测任务中展现出卓越的能效和性能，还通过多模态信息对齐增强了临床相关性。通过融入眼动数据，该模型提升了可解释性和泛化能力，为神经形态计算在医疗健康领域的应用开辟了新方向。