用距离变换增强MRI数据，提升阿尔茨海默病检测模型泛化能力
Distance Transform Guided Mixup for Alzheimer's Detection
- 基于脑部MRI的距离变换分层，跨样本混合生成新图像
- 在ADNI和AIBL数据集上均提升模型泛化性能
- 适合处理医疗影像类不平衡数据的场景
阿尔茨海默病检测旨在开发高精度模型以实现早期诊断。尽管卷积神经网络和视觉变压器方法已取得显著进展，但医学数据集普遍存在类别不平衡、成像协议差异及数据多样性不足等问题，严重制约模型泛化能力。为此，本研究聚焦单域泛化，改进经典的Mixup方法：计算MRI扫描的距离变换，将其空间分割为多层，再融合不同样本的层以生成增强图像。该方法在保持脑结构完整性的前提下生成多样化数据。实验结果表明，该方法在ADNI和AIBL数据集上均提升了模型泛化性能。
原文摘要 · Abstract (English)
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.
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