AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction
This work addresses the need for automated graph construction in medical diagnosis using multimodal data, offering a domain-specific incremental improvement over existing methods.
The paper tackled the problem of manually defining graph adjacency matrices in Graph Convolutional Networks for disease prediction by proposing an encoder that automatically selects phenotypic measures and calculates edge weights, along with a multi-layer aggregation architecture to prevent over-smoothing; experimental results showed significant improvements in diagnostic accuracy for Autism spectrum disorder and breast cancer.
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.