PopuDet: Autism Spectrum Disorder Detection in Population Graphs via Micro-macro Relationship Construction and Multi-feature Fusion

Population graphs are crucial for assessing clinical risk and enhancing the accuracy of Autism Spectrum Disorder (ASD) detection. Nevertheless, the current population graph construction overlooks the balance between biological signals and clinical manifestations, leading to relationship deviation within the population graph and poor detection performance. To address this challenge, we propose a novel approach for ASD Detection in Population Graphs (PopuDet) via Micro-macro Relationship Construction (MmRC) and Multi-feature Fusion (MF). Specifically, our method utilizes the MmRC module to construct a multi-scale population graph balancing the relationships between biological signals synchrony and clinical subtype groups. Subsequently, the MF module learns high-level graph representations at different scales for adaptive fusion to achieve precise ASD detection. Extensive experiments validate the efficiency of PopuDet, highlighting its superior performance over current state-of-the-art methods. Our source code is available at https://github.com/xuting99/PopuDet.