Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful mean for Computer Aided Diagnosis (CADx). This approach requires building a population graph to aggregate structural information, where the graph adjacency matrix represents the relationship between nodes. Until now, this adjacency matrix is usually defined manually based on phenotypic information. In this paper, we propose an encoder that automatically selects the appropriate phenotypic measures according to their spatial distribution, and uses the text similarity awareness mechanism to calculate the edge weights between nodes. The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information. In addition, a novel graph convolution network architecture using multi-layer aggregation mechanism is proposed. The structure can obtain deep structure information while suppressing over-smooth, and increase the similarity between the same type of nodes. Experimental results on two databases show that our method can significantly improve the diagnostic accuracy for Autism spectrum disorder and breast cancer, indicating its universality in leveraging multimodal data for disease prediction.

翻译：最近,图变网络(GCNs)被证明是计算机辅助诊断(CADx)的有力工具。 这种方法要求建立人口图以汇总结构信息, 图形相邻矩阵代表节点之间的关系。 到目前为止, 这个相邻矩阵通常是根据对口信息手工定义的。 在本文中, 我们提议了一个编码器, 自动根据空间分布选择适当的量度, 并使用文本相似的感知机制来计算节点之间的边缘重量。 编码器可以使用对最终结果有积极影响的预感测量器自动构建人口图, 并进一步实现多式信息的融合。 此外, 还提议了一个使用多层集成机制的新型图变形网络结构结构。 结构可以获取深层的结构信息, 同时抑制超移动, 并增加同一类型节点之间的相似性。 两个数据库的实验结果显示, 我们的方法可以显著提高自闭谱紊乱和乳腺癌的诊断准确度, 表明其在利用多式联运数据预测中的普遍性。