Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
Diagnosing Alzheimer’s disease often depends on costly neuroimaging techniques such as MRIs and PET scans, which are not always accessible and can place a significant financial burden on healthcare systems. Existing clinical workflows lack a reliable way to determine which patients truly require these advanced tests, resulting in either unnecessary imaging or delayed and inaccurate diagnoses. To address this challenge, we propose the Uncertainty-Driven Dual-view (UDD) model, a multi-stage framework that integrates low-cost clinical and structural data with uncertainty-aware learning. The model first generates predictions using accessible data and quantifies its confidence, referring only high-uncertainty cases for further evaluation with expensive imaging modalities. This selective escalation strategy enables more efficient use of resources while preserving diagnostic accuracy. By combining multi-modal learning with uncertainty-guided decision making, the proposed approach offers a cost-effective and scalable solution for early Alzheimer’s disease screening.
Included in
UR-147-188 Staged Multi-Modal Alzheimer Classification using Uncertainty Quantification
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Diagnosing Alzheimer’s disease often depends on costly neuroimaging techniques such as MRIs and PET scans, which are not always accessible and can place a significant financial burden on healthcare systems. Existing clinical workflows lack a reliable way to determine which patients truly require these advanced tests, resulting in either unnecessary imaging or delayed and inaccurate diagnoses. To address this challenge, we propose the Uncertainty-Driven Dual-view (UDD) model, a multi-stage framework that integrates low-cost clinical and structural data with uncertainty-aware learning. The model first generates predictions using accessible data and quantifies its confidence, referring only high-uncertainty cases for further evaluation with expensive imaging modalities. This selective escalation strategy enables more efficient use of resources while preserving diagnostic accuracy. By combining multi-modal learning with uncertainty-guided decision making, the proposed approach offers a cost-effective and scalable solution for early Alzheimer’s disease screening.