Distance Transform Guided Mixup for Alzheimer's Detection

2025-05-28

AI Summary

Early diagnosis of Alzheimer’s disease (AD) is hindered by severe class imbalance in medical imaging data, substantial inter-site variability in MRI acquisition protocols, and limited sample diversity—collectively undermining model generalizability. To address these challenges, we propose Distance-Transform-guided Hierarchical Mixup (DT-HMix): a novel data augmentation framework that leverages distance transforms to encode geometric priors of brain anatomy; integrates spatial hierarchical decomposition with cross-sample layer-wise mixing; and generates high-fidelity, anatomically coherent, and diverse synthetic MRI volumes while preserving structural integrity. DT-HMix is architecture-agnostic, compatible with both CNNs and Vision Transformers (ViTs). Evaluated on the multi-center ADNI and AIBL datasets, DT-HMix significantly improves model generalization and robustness across sites and scanners, outperforming standard Mixup and CutMix baselines in classification accuracy. This work establishes a generalizable, anatomy-aware data augmentation paradigm for cross-center AD neuroimaging analysis.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Addressing class imbalance in Alzheimer's detection datasets
• Improving model generalization with single-domain techniques
• Enhancing data diversity while preserving brain structure

Innovation

• Extends mixup method for single-domain generalization
• Uses distance transform to separate MRI layers
• Combines layers from distinct samples for augmentation