通过伪形态增强与对比学习，提升阿尔茨海默病3D MRI检测的泛化能力
Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive Learning
- 引入可学习的伪形态模块生成解剖学合理的数据增强
- 在三个数据集上实现更优的跨域性能，尤其在类别不平衡下
- 适合关注医疗影像泛化与小样本场景的研究者
尽管基于MRI的阿尔茨海默病检测因深度学习模型取得了显著进展，但类别不平衡、扫描协议差异及数据集多样性不足仍限制其泛化能力。本文聚焦单域泛化设置：仅使用一个领域数据训练模型，以在分布不同的未知领域上取得最优表现。鉴于脑部形态对诊断至关重要，提出可学习的伪形态模块，结合监督对比学习，生成形状感知、解剖有意义的类特定增强，并提取鲁棒的类特定表征。在三个数据集上的实验表明，该方法在类别不平衡和成像协议差异条件下均表现出更强的性能与泛化能力。源代码将在录用后公开于 https://github.com/zobia111/SDG-Alzheimer。
原文摘要 · Abstract (English)
Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imbalance, protocol variations, and limited dataset diversity often hinder their generalization capacity. To address this issue, this article focuses on the single domain generalization setting, where given the data of one domain, a model is designed and developed with maximal performance w.r.t. an unseen domain of distinct distribution. Since brain morphology is known to play a crucial role in Alzheimer's diagnosis, we propose the use of learnable pseudo-morphological modules aimed at producing shape-aware, anatomically meaningful class-specific augmentations in combination with a supervised contrastive learning module to extract robust class-specific representations. Experiments conducted across three datasets show improved performance and generalization capacity, especially under class imbalance and imaging protocol variations. The source code will be made available upon acceptance at https://github.com/zobia111/SDG-Alzheimer.
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