📝 SummarXiv

Abstract

Brain graph learning has demonstrated significant achievements in the fields of neuroscience and artificial intelligence. However, existing methods struggle to selectively learn disease-related knowledge, leading to heavy parameters and computational costs. This challenge diminishes their efficiency, as well as limits their practicality for real-world clinical applications. To this end, we propose a lightweight Brain PathoGraph Learning (BrainPoG) model that enables efficient brain graph learning by pathological pattern filtering and pathological feature distillation. Specifically, BrainPoG first contains a filter to extract the pathological pattern formulated by highly disease-relevant subgraphs, achieving graph pruning and lesion localization. A PathoGraph is therefore constructed by dropping less disease-relevant subgraphs from the whole brain graph. Afterwards, a pathological feature distillation module is designed to reduce disease-irrelevant noise features and enhance pathological features of each node in the PathoGraph. BrainPoG can exclusively learn informative disease-related knowledge while avoiding less relevant information, achieving efficient brain graph learning. Extensive experiments on four benchmark datasets demonstrate that BrainPoG exhibits superiority in both model performance and computational efficiency across various brain disease detection tasks.