论文标题
ISO-CAPSNET：用于大脑图表示学习的同构胶囊网络
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation Learning
论文作者
论文摘要
脑图表示学习是脑部疾病诊断的基本技术。近年来，学术和工业社区的巨大努力都致力于大脑图表的学习。最近引入的同构神经网络（ISONN）可以自动学习大脑图中的子图模式的存在，这也是迄今为止这种情况下的最新脑形图表学习方法。但是，Isonn无法捕获子图模式的方向，这可能使学习的表示形式在许多情况下是无用的。在本文中，我们通过引入图形同构胶囊以进行有效的脑图表示学习，提出了一种新的ISO-CAPSNET（同构胶囊NET）模型。基于胶囊动态路由，除了子图模式存在置信度得分外，ISO-CAPSNET还可以学习其他富含子图的属性，包括位置，大小和方向，用于计算班级数字胶囊。我们已经将ISO-CAPSNET与经典和最先进的脑图表示方法与四个Brain Graph基准数据集的广泛实验进行了比较。实验结果还证明了ISO-CAPSNET的有效性，ISO-CAPSNET可以超过基线方法，并取得了重大改进。
Brain graph representation learning serves as the fundamental technique for brain diseases diagnosis. Great efforts from both the academic and industrial communities have been devoted to brain graph representation learning in recent years. The isomorphic neural network (IsoNN) introduced recently can automatically learn the existence of sub-graph patterns in brain graphs, which is also the state-of-the-art brain graph representation learning method by this context so far. However, IsoNN fails to capture the orientations of sub-graph patterns, which may render the learned representations to be useless for many cases. In this paper, we propose a new Iso-CapsNet (Isomorphic Capsule Net) model by introducing the graph isomorphic capsules for effective brain graph representation learning. Based on the capsule dynamic routing, besides the subgraph pattern existence confidence scores, Iso-CapsNet can also learn other sub-graph rich properties, including position, size and orientation, for calculating the class-wise digit capsules. We have compared Iso-CapsNet with both classic and state-of-the-art brain graph representation approaches with extensive experiments on four brain graph benchmark datasets. The experimental results also demonstrate the effectiveness of Iso-CapsNet, which can out-perform the baseline methods with significant improvements.
