A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging

IF 0.1 Q4 REMOTE SENSING
GeoMedia Pub Date : 2022-10-29 DOI:10.48550/arXiv.2210.16670
N. Shehata, Wulfie Bain, Ben Glocker, J. Wolterink, Angelica I. Avilés-Rivero, E. Bekkers, Shehata Bain Glocker
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引用次数: 1

Abstract

Graph neural networks have emerged as a promising approach for the analysis of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays an important role for modelling anatomical structures, and shape classification can be used in computer aided diagnosis and disease detection. However, with a plethora of options, the best architectural choices for medical shape analysis using GNNs remain unclear. We conduct a comparative analysis to provide practitioners with an overview of the current state-of-the-art in geometric deep learning for shape classification in neuroimaging. Using biological sex classification as a proof-of-concept task, we find that using FPFH as node features substantially improves GNN performance and generalisation to out-of-distribution data; we compare the performance of three alternative convolutional layers; and we reinforce the importance of data augmentation for graph based learning. We then confirm these results hold for a clinically relevant task, using the classification of Alzheimer's disease.
神经成像中图形神经网络用于形状分类的比较研究
图神经网络已成为分析非欧几里得数据(如网格)的一种很有前途的方法。在医学成像中,网状数据在解剖结构建模方面发挥着重要作用,形状分类可用于计算机辅助诊断和疾病检测。然而,由于有太多的选择,使用GNN进行医学形状分析的最佳架构选择仍然不清楚。我们进行了一项比较分析,为从业者提供了神经成像中用于形状分类的几何深度学习的最新技术概述。使用生物性别分类作为概念验证任务,我们发现使用FPFH作为节点特征显著提高了GNN的性能和对分布外数据的泛化能力;我们比较了三种可选卷积层的性能;并且我们强调了数据扩充对于基于图的学习的重要性。然后,我们使用阿尔茨海默病的分类,确认这些结果适用于临床相关任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
GeoMedia
GeoMedia REMOTE SENSING-
自引率
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发文量
11
审稿时长
8 weeks
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