Improving brain disorder diagnosis with advanced brain function representation and Kolmogorov-Arnold Networks

Tyler Ward, Abdullah Al Zubaer Imran
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:1723-1739, 2026.

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 this Paper

BibTeX
@InProceedings{pmlr-v301-ward26a, title = {Improving brain disorder diagnosis with advanced brain function representation and Kolmogorov-Arnold Networks}, author = {Ward, Tyler and Imran, Abdullah Al Zubaer}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {1723--1739}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/ward26a/ward26a.pdf}, url = {https://proceedings.mlr.press/v301/ward26a.html}, 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.} }
Endnote
%0 Conference Paper %T Improving brain disorder diagnosis with advanced brain function representation and Kolmogorov-Arnold Networks %A Tyler Ward %A Abdullah Al Zubaer Imran %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-ward26a %I PMLR %P 1723--1739 %U https://proceedings.mlr.press/v301/ward26a.html %V 301 %X 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.
APA
Ward, T. & Imran, A.A.Z.. (2026). Improving brain disorder diagnosis with advanced brain function representation and Kolmogorov-Arnold Networks. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:1723-1739 Available from https://proceedings.mlr.press/v301/ward26a.html.

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