  Disease diagnosis on chest X-ray images is a challenging multi-label
classification task. Previous works generally classify the diseases
independently on the input image without considering any correlation among
diseases. However, such correlation actually exists, for example, Pleural
Effusion is more likely to appear when Pneumothorax is present. In this work,
we propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that
presents a novel view of investigating the inter-dependency among different
diseases by using a dynamic learnable adjacency matrix in graph structure to
improve the diagnosis accuracy. To learn more natural and reliable correlation
relationship, we feed each node with the image-level individual feature map
corresponding to each type of disease. To our knowledge, our method is the
first to build a graph over the feature maps with a dynamic adjacency matrix
for correlation learning. To further deal with a practical issue of incomplete
labels, DD-GCN also utilizes an adaptive loss and a curriculum learning
strategy to train the model on incomplete labels. Experimental results on two
popular chest X-ray (CXR) datasets show that our prediction accuracy
outperforms state-of-the-arts, and the learned graph adjacency matrix
establishes the correlation representations of different diseases, which is
consistent with expert experience. In addition, we apply an ablation study to
demonstrate the effectiveness of each component in DD-GCN.
