Distance Transform Guided Mixup for Alzheimer's Detection
Journal:
arXiv
Published Date:
May 28, 2025
Abstract
Alzheimer's detection efforts aim to develop accurate models for early
disease diagnosis. Significant advances have been achieved with convolutional
neural networks and vision transformer based approaches. However, medical
datasets suffer heavily from class imbalance, variations in imaging protocols,
and limited dataset diversity, which hinder model generalization. To overcome
these challenges, this study focuses on single-domain generalization by
extending the well-known mixup method. The key idea is to compute the distance
transform of MRI scans, separate them spatially into multiple layers and then
combine layers stemming from distinct samples to produce augmented images. The
proposed approach generates diverse data while preserving the brain's
structure. Experimental results show generalization performance improvement
across both ADNI and AIBL datasets.
