用图结构改进脑影像自监督学习，提升模型泛化能力。
Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations
- 将层级功能最大相关算法适配到图数据，基于核空间密度比分解建模依赖关系
- 在5个数据集上实现竞争力分类性能，跨数据集迁移效果显著
- 适合追求模型泛化性的神经影像研究者，尤其适用于小样本场景
功能性磁共振成像（fMRI）分析面临数据集规模有限和研究间域差异大的挑战。传统受计算机视觉启发的自监督学习方法常依赖正负样本对，但在神经影像中定义合适对比关系并不容易。我们提出将近期提出的层级功能最大相关算法（HFMCA）适配至图结构的fMRI数据，通过再生核希尔伯特空间（RKHS）中的密度比分解，理论上严谨地度量统计依赖性，并应用基于HFMCA的预训练来学习鲁棒且可泛化的表示。在五个神经影像数据集上的评估表明，该方法生成的嵌入在多种分类任务中表现优异，并能有效将知识迁移到未见过的数据集。代码与补充材料见：https://github.com/fr30/mri-eigenencoder
原文摘要 · Abstract (English)
Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies. Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs, which can be problematic for neuroimaging data where defining appropriate contrasts is non-trivial. We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data, providing a theoretically grounded approach that measures statistical dependence via density ratio decomposition in a reproducing kernel Hilbert space (RKHS),and applies HFMCA-based pretraining to learn robust and generalizable representations. Evaluations across five neuroimaging datasets demonstrate that our adapted method produces competitive embeddings for various classification tasks and enables effective knowledge transfer to unseen datasets. Codebase and supplementary material can be found here: https://github.com/fr30/mri-eigenencoder
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