用新型神经网络提升自闭症诊断准确率，避免传统脑图谱偏差
Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks
- 用Transformer+KAN替代MLP，更精准捕捉脑功能连接特征
- 在多个模型配置下显著提升自闭症诊断性能
- 适合关注脑疾病智能诊断与神经网络结构创新的研究者
量化功能连接（FC）是多种脑疾病诊断的关键指标，传统方法依赖预定义脑图谱，易引发选择偏差且缺乏特异性。为此，我们提出一种基于Transformer的分类网络ABFR-KAN，通过有效脑功能表征辅助自闭症谱系障碍（ASD）诊断。该模型用柯尔莫戈洛夫-阿诺德网络（KAN）块替代传统的多层感知机（MLP）组件。大量实验表明，ABFR-KAN在不同模型架构配置下均能有效提升ASD诊断效果。代码已公开于https://github.com/tbwa233/ABFR-KAN。
原文摘要 · Abstract (English)
Quantifying functional connectivity (FC), a vital metric for the diagnosis of various brain disorders, traditionally relies on the use of a pre-defined brain atlas. However, using such atlases can lead to issues regarding selection bias and lack of regard for specificity. Addressing this, we propose a novel transformer-based classification network (ABFR-KAN) with effective brain function representation to aid in diagnosing autism spectrum disorder (ASD). ABFR-KAN leverages Kolmogorov-Arnold Network (KAN) blocks replacing traditional multi-layer perceptron (MLP) components. Thorough experimentation reveals the effectiveness of ABFR-KAN in improving the diagnosis of ASD under various configurations of the model architecture. Our code is available at https://github.com/tbwa233/ABFR-KAN
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