A Two-Scan Deep Learning Model for Predicting Dementia in Mild Cognitive Impairment
Predicting which people with mild cognitive impairment (MCI) will develop dementia matters for starting treatment early, yet computational work on structural brain imaging has relied almost entirely on a single scan. We propose TAFNet, a temporal attention fusion network that combines a baseline and a follow-up T1-weighted scan. A pretrained Siamese encoder represents each scan, and a fusion module combines the two through anatomical difference, cross-temporal attention and joint context, mixed by a learned per-patient gate. We evaluate it on paired scans from participants with MCI in the Alzheimer's Disease Neuroimaging Initiative, taken up to two years apart, with a dementia diagnosis w...
| Authors | Sara Fin, Alireza Moayedikia, David J. White et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 May 2026 |
| Last updated at source | 8 October 2026 |
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