Get Started
Home
Topics
Search
Library
Research questionCan compact pretrained brain MRI models transfer across Alzheimer’s tasks and cohorts without task-specific retraining?Limited labeled neuroimaging data makes task-specific deep learning difficult. It remains uncertain whether features learned for one brain MRI task generalize to different Alzheimer’s-related tasks and cohorts.
AI
Computer Vision
Health
Image & Video Processing
Machine Learning
Research Paper
Latest papersRecent research connected to this question, newest first.A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI TasksThe evidence concerns a 3D CNN pretrained for brain-age prediction, with its 7.18 million weights frozen and approximately 1% additional parameters trained through LoRA. Experiments cover dementia classification on ADNI, unchanged transfer to OASIS-3, MCI progression prediction using age and a cognitive score, amyloid-positivity prediction, and estimation of normalized hippocampal and white-matter-hypointensity volumes from T1-weighted MRI. Reported results include AUCs of 0.964 on held-out ADNI folds, 0.871 on OASIS-3, 0.828 for MCI progression, 0.804 for amyloid positivity, and R² values of 0.80 and 0.91 for volume estimation.research paper · Sep 4, 2026
Related questions
How can MEG foundation models become reusable across subjects, sites, and tasks despite limited training data?How can automated brain-MRI reporting compare longitudinal studies to describe subtle, distributed interval changes?How can longitudinal MRI models forecast neurodegenerative progression without confusing stable anatomy or noise with disease change?How can self-supervised representations transfer reliably across microscopy datasets with scarce labels and mismatched staining or channels?