Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks
Abstract
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. Ad- dressing 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.
Cite
Text
Ward and Imran. "Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks." Medical Imaging with Deep Learning, 2025.Markdown
[Ward and Imran. "Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks." Medical Imaging with Deep Learning, 2025.](https://mlanthology.org/midl/2025/ward2025midl-improving/)BibTeX
@inproceedings{ward2025midl-improving,
title = {{Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks}},
author = {Ward, Tyler and Imran, Abdullah Al Zubaer},
booktitle = {Medical Imaging with Deep Learning},
year = {2025},
url = {https://mlanthology.org/midl/2025/ward2025midl-improving/}
}