Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks
This work addresses brain disorder diagnosis for medical applications, but it appears incremental as it modifies existing methods with a new component.
The paper tackles the problem of diagnosing autism spectrum disorder (ASD) by addressing selection bias and specificity issues in functional connectivity metrics, proposing a transformer-based network with Kolmogorov-Arnold Network blocks that improves ASD diagnosis under various configurations.
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