Keywords: Diagnosis/Prediction, Diagnosis/Prediction, HIV-Associated Neurocognitive Disorder (HAND), Hyperbolic kernel, Multimodal fusion
Motivation: HIV-associated neurocognitive disorder (HAND) has generally been studied through a single imaging modality like functional Magnetic Resonance Imaging (fMRI), which restricts understanding of its complex neuropathological causes.
Goal(s): Our goal is to develop a multimodal approach that integrates fMRI and diffusion tensor imaging (DTI) for a more comprehensive HAND analysis.
Approach: We introduce hyperbolic kernel-based multimodal fusion (HKMF), a new method for HAND analysis, combining fMRI and DTI data using hyperbolic geometry for more comprehensive insights.
Results: Experimental results on 137 subjects show that our approach outperforms traditional methods in HAND diagnosis.
Impact: HKMF utilizes hyperbolic geometry, which is well-suited for capturing complex hierarchies in neuroimaging applications. This approach not only enhances HAND analysis but also can be extended to other medical imaging applications involving multimodal datasets.
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