BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
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.
