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.
arXiv:2605.28397v2 Announce Type: replace Abstract: Predicting which people with mild cognitive impairment will develop dementia matters for early treatment. Yet structural imaging models have relied...
arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...
The study presents an explainable multimodal deep‑learning framework that combines a 3D CNN for T1‑weighted MRI with a feedforward network for harmonized clinical and demographic data to diagnose Alzheimer’s disease. Using 6,479 ADNI records and 1,703 OASIS‑3 records, the authors compare various model configurations on three‑way and pairwise diagnostic tasks, finding that performance and explanations vary by task, modality, fusion strategy, and cohort. SHAP and Integrated Gradients consistently highlight the MMSE score as the most influential tabular feature, while CAM‑based explanations differ across model setups and cohorts, indicating that explainability is not a stable property under cohort shift.
The study investigates whether a compact, supervised 3D CNN pretrained for brain‑age prediction can act as a reusable foundation model for various Alzheimer's‑related neuroimaging tasks. By freezing the 7.18 million weights and adding only ~1 % of trainable parameters via Low‑Rank Adaptation, the model achieved high performance across six experiments, including dementia classification, MCI progression prediction, amyloid positivity detection, and volume estimation of hippocampal and white matter hypointensities. The results demonstrate that the pretrained brain‑age model generalizes well to new datasets without retraining, offering a data‑efficient alternative to larger networks.
arXiv:2608. 11762v1 Announce Type: cross Abstract: Alzheimer's disease is a leading cause of death with no cure.
arXiv:2606. 20037v1 Announce Type: new Abstract: Alzheimer's disease (AD) is an irreversible neurodegenerative disorder and a leading cause of death worldwide.
arXiv:2607. 07091v1 Announce Type: cross Abstract: In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits.
arXiv:2607. 15047v1 Announce Type: cross Abstract: Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries.
In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these multimodal observations may improve diagnostic assessment, but naive fusion can degrade performance when MRI is noisy or intermittently unavailable.
The paper introduces a lightweight Cross‑Layer Fusion Adapter (CLFA) that adapts the CLIP vision‑language model for handwriting‑based Alzheimer's disease screening. CLFA inserts multi‑level adapters into a frozen visual encoder, fusing cross‑layer features with depthwise 2D convolutions to capture both local stroke irregularities and higher‑level handwriting structure. On the Darwin dataset, CLFA achieves 74.63% AUC, 74.85% accuracy, and 73.72% F1, outperforming the best competing model by 2.15, 1.79, and 1.87 percentage points across 600 task‑disjoint source‑target pairs.
M$^2$PFN is an end‑to‑end multimodal framework that extends the TabPFN in‑context learning engine to Alzheimer’s disease diagnosis by aligning 3D‑MRI and tabular features in a shared subspace. It performs differentiable inference through TabPFN’s transformer, back‑propagates gradients into the encoders, and incorporates a frozen tabular‑only prediction via a gated shortcut. On the ADNI cohort it achieves 65.55 % macro‑F1 and 82.21 % macro‑AUC, surpassing unimodal and multimodal baselines, and it generalizes to external cohorts without retraining.
arXiv:2607. 11656v1 Announce Type: cross Abstract: Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data.