Distance Transform Guided Mixup for Alzheimer's Detection

TL;DR

This paper introduces a novel data augmentation method using distance transform guided mixup for Alzheimer's detection, improving model generalization across different datasets by preserving brain structure during augmentation.

Contribution

It extends the mixup technique with distance transform-based layering to enhance data diversity and model robustness in Alzheimer's MRI classification.

Findings

Improved generalization performance on ADNI and AIBL datasets.

Enhanced data diversity while maintaining brain structure integrity.

Effective handling of class imbalance and dataset variability.

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.