用图神经网络提升小样本脑影像分析效果，性能提升12%。
Using Graph Convolutional Networks to Address fMRI Small Data Problems
- 将脑区连接图建模为图结构，用谱图卷积进行信息传播。
- 在相同数据下比传统深度学习方法提升约12%准确率。
- 通过减少三角不等式数量增强数据平滑性，适合小样本医学影像研究。
尽管神经影像数据分析取得了重大进展，但训练数据不足仍是主要挑战。诊断类任务数据充足，但在预测治疗反应（预后）等复杂问题上，数据高度集中且有限。本文利用图神经网络解决医学影像的小样本学习问题。由于患者数据本身可表示为图结构（如感兴趣区域的连接图），我们采用连接数据的谱表示，实现高效信息传播，在相同数据条件下，性能相比传统深度学习方法提升约12%。实验表明，该方法优势源于数据平滑效应，可通过减少三角不等式数量来衡量，从而更好地满足传递性要求。
原文摘要 · Abstract (English)
Although great advances in the analysis of neuroimaging data have been made, a major challenge is a lack of training data. This is less problematic in tasks such as diagnosis, where much data exists, but particularly prevalent in harder problems such as predicting treatment responses (prognosis), where data is focused and hence limited. Here, we address the learning from small data problems for medical imaging using graph neural networks. This is particularly challenging as the information about the patients is themselves graphs (regions of interest connectivity graphs). We show how a spectral representation of the connectivity data allows for efficient propagation that can yield approximately 12\% improvement over traditional deep learning methods using the exact same data. We show that our method's superior performance is due to a data smoothing result that can be measured by closing the number of triangle inequalities and thereby satisfying transitivity.
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