IEEE Trans Pattern Anal Mach Intell. 2022 Feb;44(2):864-876. doi: 10.1109/TPAMI.2020.3028391. Epub 2022 Jan 10.
Brain surface analysis is essential to neuroscience, however, the complex geometry of the brain cortex hinders computational methods for this task. The difficulty arises from a discrepancy between 3D imaging data, which is represented in Euclidean space, and the non-Euclidean geometry of the highly-convoluted brain surface. Recent advances in machine learning have enabled the use of neural networks for non-Euclidean spaces. These facilitate the learning of surface data, yet pooling strategies often remain constrained to a single fixed-graph. This paper proposes a new learnable graph pooling method for processing multiple surface-valued data to output subject-based information. The proposed method innovates by learning an intrinsic aggregation of graph nodes based on graph spectral embedding. We illustrate the advantages of our approach with in-depth experiments on two large-scale benchmark datasets. The ablation study in the paper illustrates the impact of various factors affecting our learnable pooling method. The flexibility of the pooling strategy is evaluated on four different prediction tasks, namely, subject-sex classification, regression of cortical region sizes, classification of Alzheimer's disease stages, and brain age regression. Our experiments demonstrate the superiority of our learnable pooling approach compared to other pooling techniques for graph convolutional networks, with results improving the state-of-the-art in brain surface analysis.
脑表面分析对于神经科学至关重要，然而，大脑皮层的复杂几何形状阻碍了该任务的计算方法。这种困难源于 3D 成像数据与高度卷曲的大脑表面的非欧几里得几何形状之间的差异。机器学习的最新进展使得神经网络能够用于非欧几里得空间。这些方法促进了表面数据的学习，但池化策略通常仍然局限于单个固定图。本文提出了一种新的可学习图池化方法，用于处理多个表面值数据以输出基于主体的信息。所提出的方法通过基于图谱嵌入学习图节点的内在聚合来创新。我们通过在两个大规模基准数据集上进行深入实验来展示我们方法的优势。本文的消融研究说明了影响我们可学习池化方法的各种因素的影响。我们在四个不同的预测任务上评估了池化策略的灵活性，即主体性别分类、皮质区域大小的回归、阿尔茨海默病阶段的分类和大脑年龄回归。我们的实验表明，与图卷积网络的其他池化技术相比，我们的可学习池化方法具有优越性，结果提高了大脑表面分析的最新水平。
