AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer's Disease Diagnosis
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. Integrating these multimodal observations may improve diagnostic assessment, but naive fusion can degrade performance when MRI is noisy or intermittently unavailable. We propose AT-Attn, a temporal-aware multimodal framework that combines Change-and-Time encoding, time-biased asymmetric cross-attention, and gated fusion to integrate MRI with longitudinal clinical information. We evaluate AT-Attn on an MRI-retained
This research is emerging as AI methodologies, particularly in multimodal data fusion and temporal awareness, are maturing, offering new avenues for complex medical diagnostics.
Advanced AI methods for multimodal longitudinal data processing can significantly improve early and accurate diagnosis of neurodegenerative diseases, impacting healthcare systems, pharmaceutical development, and patient outcomes.
This marks a step towards more robust and reliable AI-powered diagnostic tools that can handle real-world clinical data imperfections, reducing reliance on perfect data capture for effective analysis.
- · AI healthcare tech companies
- · Medical diagnostic device manufacturers
- · Neurology specialists
- · Pharmaceutical companies researching AD
- · Traditional diagnostic methods
- · Healthcare providers with limited AI integration
Improved early diagnosis rates for Alzheimer's disease become possible, leading to earlier interventions.
The demand for specialized longitudinal multimodal diagnostic AI systems increases, driving investment in this niche.
Personalized treatment plans based on more accurate and timely diagnoses become more common, shifting AD management paradigms.
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Read at arXiv cs.AI