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Deep Forest with Cross-shaped Window Scanning Mechanism to Extract Topological Features

机译：具有十字形窗口扫描机制的深林提取拓扑特征

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Deep neural networks have been successfully applied to the classification of brain networks. However, the high-dimensional and small-scale properties of the brain network data limit their extensive applications. To solve this problem, this paper proposes a new deep forest framework with cross-shaped window scanning mechanism (DF-CWSM) to extract topological features for the classification of brain networks. The cross-shaped window scanning mechanism is designed to extract the node-level and the edge-level features respectively that have meaningful interpretations in terms of corresponding network topologies. Based on the classification framework, we firstly implement the feature transformation of brain networks by the multi-level topological feature extraction. Then a cascade forest structure is used to learn the hierarchical features layer by layer. And the results of the last level of cascade forests are integrated to make the final classification. We evaluated the proposed framework on the ABIDE I data set. Experimental results show that our proposed framework can not only achieve competitive classification performance but also accurately identify the abnormal brain regions associated with ASD.
机译：深度神经网络已成功应用于脑网络的分类。但是，脑网络数据的高维和小规模属性限制了它们的广泛应用。为了解决这个问题，本文提出了一种新的具有交叉形窗口扫描机制（DF-CWSM）的深林框架，以提取拓扑特征进行脑网络分类。十字形窗口扫描机制旨在提取分别具有相应网络拓扑意义的有意义的解释的节点级和边缘级特征。在分类框架的基础上，我们首先通过多层次的拓扑特征提取实现脑网络的特征转换。然后使用级联林结构逐层学习层次特征。并综合了最后一级森林的结果，以进行最终分类。我们在ABIDE I数据集上评估了建议的框架。实验结果表明，我们提出的框架不仅可以实现竞争性分类性能，而且可以准确识别与ASD相关的异常大脑区域。

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