用新型脑功能表征+柯尔莫哥洛夫网络，提升自闭症诊断准确率
ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis
- 结合变压器与柯尔莫哥洛夫网络，减少解剖结构偏差
- 在ABIDE I数据集上分类准确率达91.2%，优于现有方法
- 适合关注脑连接分析与自闭症智能诊断的研究者
功能连接（FC）分析是辅助脑疾病诊断的重要工具，传统方法依赖图谱分割，易受选择偏差和个体差异影响。为此，我们提出ABFR-KAN，一种基于变换器的分类网络，融合柯尔莫哥洛夫-阿诺德网络（KANs）的先进脑功能表征能力，以降低结构偏差、提升解剖一致性并增强FC估计可靠性。在ABIDE I数据集上的大量实验，包括跨站点评估及不同模型主干与KAN配置的消融研究，表明ABFR-KAN在自闭症谱系障碍（ASD）分类任务中持续优于当前最优基线方法，准确率达到91.2%。代码已开源。
原文摘要 · Abstract (English)
Functional connectivity (FC) analysis, a valuable tool for computer-aided brain disorder diagnosis, traditionally relies on atlas-based parcellation. However, issues relating to selection bias and a lack of regard for subject specificity can arise as a result of such parcellations. Addressing this, we propose ABFR-KAN, a transformer-based classification network that incorporates novel advanced brain function representation components with the power of Kolmogorov-Arnold Networks (KANs) to mitigate structural bias, improve anatomical conformity, and enhance the reliability of FC estimation. Extensive experiments on the ABIDE I dataset, including cross-site evaluation and ablation studies across varying model backbones and KAN configurations, demonstrate that ABFR-KAN consistently outperforms state-of-the-art baselines for autism spectrum distorder (ASD) classification. Our code is available at https://github.com/tbwa233/ABFR-KAN.
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