AdaptAD: Foundation Model Adaptation with Missing Modality Robustness for Alzheimer's Disease Staging Across the Disease Continuum

arXiv:2611.00084v1 Announce Type: cross Abstract: Staging Alzheimer's disease (AD) across its continuum, from cognitively normal (CN) through mild cognitive impairment (MCI) to dementia from a single structural MRI remains difficult because 3-D networks overfit small cohorts, and FreeSurfer morphometry is often unavailable. We present AdaptAD, which reads a registered T1-weighted scan as 16 anatomically placed 2.5-D slices through the medial temporal lobe and adapts a frozen ImageNet-pretrained ViT-B/16 with low-rank adaptation (LoRA), training 3.5 M of 89 M parameters. Morphometry enters as tokens when present, or as a learned [MISSING] token, and a missing-aware stacker fuses both experts. Under a pre-registered protocol, ten methods were compared for three-class CN/MCI/AD staging on identical 5-fold splits of 597 ADNI participants, 29% without FreeSurfer, with Holm-corrected paired bootstrap tests. The locked model was tested once on 993 OASIS-3 participants (988 scored by every method). On ADNI, AdaptAD achieved a three-class macro-AUC of 0.745 (95% CI 0.709 to 0.781), ranking first and significantly exceeding full fine-tuning, ShaSpec and a 3-D CNN. On OASIS-3 it reached 0.780 (0.748 to 0.814), and on the CDR-based label (0.804) it significantly outperformed every 3-D and image-tabular method. LoRA matched full fine-tuning with 25 times fewer trainable parameters, and removing morphometry changed external macro-AUC by less than 0.003. The disease score tracked MMSE and CDR-SB within diagnostic groups and stratified MCI-to-dementia progression (log-rank p < 0.001), while saliency concentrated in medial temporal and periventricular regions. A parameter-efficient 2-D foundation model can therefore stage the full AD continuum from a routine T1 scan without morphometry, offering a scalable route to memory-clinic deployment.