Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI

Detecting amyloid-$β$ (A$β$) positivity is crucial for early diagnosis of Alzheimer's disease but typically requires PET imaging, which is costly, invasive, and not widely accessible, limiting its use for population-level screening. We address this gap by proposing a PET-guided knowledge distillation framework that enables A$β$ prediction from MRI alone, without requiring non-imaging clinical covariates or PET at inference. Our approach employs a BiomedCLIP-based teacher model that learns PET-MRI alignment via cross-modal attention and triplet contrastive learning with PET-informed (Centiloid-aware) online negative sampling. An MRI-only student then mimics the teacher via feature-level and logit-level distillation. Evaluated across four MRI contrasts (T1w, T2w, FLAIR, T2*) and two independent datasets, our approach demonstrates effective knowledge transfer (best AUC: 0.74 on OASIS-3, 0.68 on ADNI) while maintaining interpretability and eliminating the need for clinical variables. Saliency analysis confirms that predictions focus on anatomically relevant cortical regions, supporting the clinical viability of PET-free A$β$ screening. Code is available at https://github.com/FrancescoChiumento/pet-guided-mri-amyloid-detection.

Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images

Alzheimer's Disease (AD), which is the most common cause of dementia, is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of the disease is crucial for making treatment decisions. However, most of the literature on computer-assisted detection of AD focuses on classifying brain images into one of three major categories: healthy, MCI, and AD; or categorising MCI patients into one of (1) progressive: those who progress from MCI to AD at a future examination time during a given study period, and (2) stable: those who stay as MCI and never progress to AD. This misses the opportunity to accurately identify the trajectory of progressive MCI patients. In this paper, we revisit the brain image classification task for AD identification and re-frame it as an ordinal classification task to predict how close a patient is to the severe AD stage. To this end, we select progressive MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and construct an ordinal dataset with a prediction target that indicates the time to progression to AD. We train a siamese network model to predict the time to onset of AD based on MRI brain images. We also propose a weighted variety of siamese networks and compare its performance to a baseline model. Our evaluations show that incorporating a weighting factor to siamese networks brings considerable performance gain at predicting how close input brain MRI images are to progressing to AD.