GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks

• URL: http://arxiv.org/abs/2504.00946v1
• Date: Tue, 01 Apr 2025 16:31:00 GMT
• Title: GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks
• Authors: Tianqi Ding, Dawei Xiang, Keith E Schubert, Liang Dong,
• Abstract summary: We propose a novel single-modal framework that integrates Kolmogorov-Arnold Networks (KAN) into Graph Convolutional Networks (GCNs) to enhance both diagnostic accuracy and interpretability.<n>GCN-KAN outperforms traditional GCNs by 4-8% in classification accuracy while providing interpretable insights into key brain regions associated with Alzheimer's Disease (AD)<n>This approach offers a robust and explainable tool for early AD diagnosis.
• Score: 0.6282459656801734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that poses significant diagnostic challenges due to its complex etiology. Graph Convolutional Networks (GCNs) have shown promise in modeling brain connectivity for AD diagnosis, yet their reliance on linear transformations limits their ability to capture intricate nonlinear patterns in neuroimaging data. To address this, we propose GCN-KAN, a novel single-modal framework that integrates Kolmogorov-Arnold Networks (KAN) into GCNs to enhance both diagnostic accuracy and interpretability. Leveraging structural MRI data, our model employs learnable spline-based transformations to better represent brain region interactions. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, GCN-KAN outperforms traditional GCNs by 4-8% in classification accuracy while providing interpretable insights into key brain regions associated with AD. This approach offers a robust and explainable tool for early AD diagnosis.

Related papers

• NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis [35.4733004746959]
  NeuroSymAD is a neuro-symbolic framework that synergizes neural networks with symbolic reasoning.<n>A neural network percepts brain MRI scans, while a large language model distills medical rules to guide a symbolic system in reasoning over biomarkers and medical history.
  arXiv  Detail & Related papers  (2025-03-01T14:29:39Z)
• Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging [1.074960192271861]
  This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs) By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score.
  arXiv  Detail & Related papers  (2024-10-03T05:04:45Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.<n>As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.<n>The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• SNeurodCNN: Structure-focused Neurodegeneration Convolutional Neural Network for Modelling and Classification of Alzheimer's Disease [0.0]
  Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN.
  arXiv  Detail & Related papers  (2024-01-08T14:33:57Z)
• Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images [53.235117594102675]
  Optical Coherence Tomography Angiography is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. We propose a novel deep-learning framework called Polar-Net to provide interpretable results and leverage clinical prior knowledge. We show that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD.
  arXiv  Detail & Related papers  (2023-11-10T11:49:49Z)
• Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features [119.45320143101381]
  Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks. We have studied GCNs with covariance matrices as graphs in the form of coVariance neural networks (VNNs) VNNs inherit the scale-free data processing architecture from GCNs and here, we show that VNNs exhibit transferability of performance over datasets whose covariance matrices converge to a limit object.
  arXiv  Detail & Related papers  (2023-05-02T22:15:54Z)
• Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease using EEG Data [9.601125513491835]
  We propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a correlation-based measure of power spectral density similarity.
  arXiv  Detail & Related papers  (2023-04-12T14:13:09Z)
• Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity [48.75665245214903]
  We propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling.
  arXiv  Detail & Related papers  (2022-09-22T04:17:57Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.