Interpreting Kolmogorov-Arnold Networks in Neuroimaging: A Path-Based Attribution Framework

TMLR Paper6775 Authors

Abstract: Explainability aspects of most classification models are learnt through instance-specific analysis. However, in understanding diseases, it is important to consider population-wide analysis in order to identify affected regions that are consistently seen across cohorts of diseased population. In this study, we report utility of Kolmogorov-Arnold Networks (KANs) in understanding population-wide characteristics seen in subjects affected by Alzheimer’s disease (AD). KANs offer enhanced interpretability through learnable activation functions on network edges. Thus, the learned functions reflect the characteristics of the entire span of training data. In a KAN network trained for classification, attributions through the network can be traced to understand how specific inputs influence the output label. In this study, we propose a path-based attribution framework that generates global importance maps by tracing exhaustive information flow through all potential paths. Our method scores edges using L2 norms of the learned spline and base functions. Subsequently, these scores are propagated through the network to compute path-attributions. This approach scales linearly with network depth, and is only dependent on model training and does not need further analysis on data post-hoc. Evaluation on three public AD neuroimaging datasets (OASIS, ADNI, Mendeley, totally comprising 7428 acquisitions), were carried out on 3D brain volumes as well as 2D brain slices. The corresponding KAN test accuracies are $93.24\%$, $81.85\%$, and $91.25\%$ on OASIS, ADNI, and Mendeley datasets, respectively. Alongside, improved performance via metrics such as Insertion AUC, Deletion AUC and Sufficiency, is also demonstrated. The generated attribution maps identify clinically meaningful regions including the body and genu of corpus callossum, corona radiata, bilateral caudate nuclei, medial prefrontal cortex and temporal lobe structures, aligned with established AD pathology literature. By providing voxel-level global attributions as network-intrinsic properties, our framework addresses a critical gap in medical AI interpretability and supports clinical validation of AI-assisted AD diagnosis systems.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Anirbit_Mukherjee1

Submission Number: 6775