Multi-Modal Fusion with Supervised Contrastive Learning Model for Early Alzheimer’s Disease Diagnosis and Multi-Modal Biomarker Identification
Key Points
Founded on multi-modal fusion, the study identifies biomarkers critical for early Alzheimer’s disease diagnosis.
Using supervised contrastive learning, the model demonstrates enhanced accuracy in distinguishing early disease stages with greater than 85% sensitivity.
Analysis utilizes neural networks across various data types, including imaging and genetic biomarkers, to validate effectiveness.
Highlights the pressing need for early detection strategies, as timely intervention could significantly improve patient outcomes.
Multi-Modal Fusion with Supervised Contrastive Learning Model for Early Alzheimer’s Disease Diagnosis and Multi-Modal Biomarker Identification | Synapse