论文标题
拓扑感知的生成对抗网络，用于从单个脑图的多个脑图联合预测
Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph
论文作者
论文摘要
最近已经提出了基于生成对抗网络（GAN）的几项基于生成对抗性网络（GAN）的作品，以预测单个模态的一组医学图像（例如，T1 MRI的FLAIR MRI）。但是，此类框架主要是为了在图像上操作，从而限制了它们对非欧几里得几何数据（例如脑图）的推广性。虽然越来越多的连接组研究表明，有望包括用于诊断神经系统疾病的大脑图，但没有设计用于从源脑图预测的多个目标脑图预测的几何深度学习工作。尽管有动力，在过去的两年中，图生成领域已经获得了，但现有作品仍有两个关键缺点。首先，大部分此类作品旨在学习一个模型，以从源域中生成一个模型。因此，它们在共同预测多个目标域的可伸缩性有限。其次，他们仅考虑图形的全局拓扑尺度（即图形连接结构），并在图的节点刻度（例如，图中的中央节点在图中的中央范围）忽略了局部拓扑。为了应对这些挑战，我们介绍了多格掌结构，该体系结构不仅可以预测单个大脑图的多个大脑图，而且还保留了每个目标图的拓扑结构。 Its three core contributions lie in: (i) designing a graph adversarial auto-encoder for jointly predicting brain graphs from a single one, (ii) handling the mode collapse problem of GAN by clustering the encoded source graphs and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the reconstruction of topologically sound target brain graphs.我们的多格（Multigraphgan）明显胜过其变体，因此从单个图中显示出其在多视图脑图生成中的巨大潜力。
Several works based on Generative Adversarial Networks (GAN) have been recently proposed to predict a set of medical images from a single modality (e.g, FLAIR MRI from T1 MRI). However, such frameworks are primarily designed to operate on images, limiting their generalizability to non-Euclidean geometric data such as brain graphs. While a growing number of connectomic studies has demonstrated the promise of including brain graphs for diagnosing neurological disorders, no geometric deep learning work was designed for multiple target brain graphs prediction from a source brain graph. Despite the momentum the field of graph generation has gained in the last two years, existing works have two critical drawbacks. First, the bulk of such works aims to learn one model for each target domain to generate from a source domain. Thus, they have a limited scalability in jointly predicting multiple target domains. Second, they merely consider the global topological scale of a graph (i.e., graph connectivity structure) and overlook the local topology at the node scale of a graph (e.g., how central a node is in the graph). To meet these challenges, we introduce MultiGraphGAN architecture, which not only predicts multiple brain graphs from a single brain graph but also preserves the topological structure of each target graph to predict. Its three core contributions lie in: (i) designing a graph adversarial auto-encoder for jointly predicting brain graphs from a single one, (ii) handling the mode collapse problem of GAN by clustering the encoded source graphs and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the reconstruction of topologically sound target brain graphs. Our MultiGraphGAN significantly outperformed its variants thereby showing its great potential in multi-view brain graph generation from a single graph.
