  Image representation is a fundamental task in computer vision. However, most
of the existing approaches for image representation ignore the relations
between images and consider each input image independently. Intuitively,
relations between images can help to understand the images and maintain model
consistency over related images, leading to better explainability. In this
paper, we consider modeling the image-level relations to generate more
informative image representations, and propose ImageGCN, an end-to-end graph
convolutional network framework for inductive multi-relational image modeling.
We apply ImageGCN to chest X-ray images where rich relational information is
available for disease identification. Unlike previous image representation
models, ImageGCN learns the representation of an image using both its original
pixel features and its relationship with other images. Besides learning
informative representations for images, ImageGCN can also be used for object
detection in a weakly supervised manner. The experimental results on 3
open-source x-ray datasets, ChestX-ray14, CheXpert and MIMIC-CXR demonstrate
that ImageGCN can outperform respective baselines in both disease
identification and localization tasks and can achieve comparable and often
better results than the state-of-the-art methods.
