arXiv:2512. 10966v3 Announce Type: replace-cross Abstract: Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data.
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arXiv:2606. 20037v1 Announce Type: new Abstract: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide.
In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these multimodal observations may improve diagnostic assessment, but naive fusion can degrade performance when MRI is noisy or intermittently unavailable.
arXiv:2607. 07091v1 Announce Type: cross Abstract: In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits.
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.
arXiv:2606. 17867v1 Announce Type: cross Abstract: Despite increasing adoption of multimodal approaches in Alzheimer's Disease (AD) research -- aimed at integrating molecular, structural, clinical, and genetic biomarkers to enhance disease characterization -- the relationships among these modalities remain poorly understood.
arXiv:2606. 19371v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a fatal disorder that destroys memory and cognitive skills in the elderly population.