AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network
  for Disease Prediction
  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.
