模仿大脑结构设计专家网络，提升阿尔茨海默病与路易体痴呆的区分准确率。
BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
- 构建分组专家网络，按疾病类型处理脑区子网络
- 通过门控机制实现专家专一化，融合全脑表征
- 在罕见病诊断中表现优越，适合神经疾病分类研究
路易体痴呆（LBD）是仅次于阿尔茨海默病（AD）的第二大常见神经退行性痴呆。早期区分AD与LBD至关重要，因治疗方案不同，但临床重叠多、异质性强、发病机制复杂且LBD病例稀少，诊断困难。尽管人工智能近年展现强大学习能力，现有方法多聚焦于“神经层级网络”设计。本文首次提出面向脑建模与诊断的系统级人工神经网络BrainNet-MoE，受大脑自下而上感知整合与自上而下调控的层次组织启发，设计疾病特异性专家组以处理不同条件下的脑区子网络；通过疾病门控机制引导专家组专业化，结合Transformer层实现各子网络间通信，生成用于下游疾病分类的全脑表征。实验表明该模型在分类精度上优于现有方法，并提供可解释性洞察，揭示脑区子网络如何贡献于不同神经退行性疾病。
原文摘要 · Abstract (English)
The Lewy body dementia (LBD) is the second most common neurodegenerative dementia after Alzheimer's disease (AD). Early differentiation between AD and LBD is crucial because they require different treatment approaches, but this is challenging due to significant clinical overlap, heterogeneity, complex pathogenesis, and the rarity of LBD. While recent advances in artificial intelligence (AI) demonstrate powerful learning capabilities and offer new hope for accurate diagnosis, existing methods primarily focus on designing "neural-level networks". Our work represents a pioneering effort in modeling system-level artificial neural network called BrainNet-MoE for brain modeling and diagnosing. Inspired by the brain's hierarchical organization of bottom-up sensory integration and top-down control, we design a set of disease-specific expert groups to process brain sub-network under different condition, A disease gate mechanism guides the specializa-tion of expert groups, while a transformer layer enables communication be-tween all sub-networks, generating a comprehensive whole-brain represen-tation for downstream disease classification. Experimental results show superior classification accuracy with interpretable insights into how brain sub-networks contribute to different neurodegenerative conditions.
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