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arXiv Computer Vision By Sara Fin, Alireza Moayedikia, David J. White, Uffe Kock Wiil, Alicia Troncoso

A Two-Scan Deep Learning Model for Predicting Dementia in Mild Cognitive Impairment

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The paper introduces TAFNet, a temporal attention fusion network that uses both a baseline and a follow‑up T1‑weighted brain scan to predict which individuals with mild cognitive impairment (MCI) will progress to dementia. The model employs a pretrained Siamese encoder for each scan and fuses the two scans through anatomical difference, cross‑temporal attention, joint context, and a learned per‑patient gate. Evaluated on paired scans from the Alzheimer’s Disease Neuroimaging Initiative, TAFNet outperforms single‑scan networks and a simple scan‑difference model, achieving significant gains in cross‑validation and maintaining high sensitivity at clinically reasonable specificity.

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