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. 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 multi-relational image modeling. We also apply ImageGCN to chest X-ray (CXR) 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 the features of related images. Besides learning informative representations for images, ImageGCN can also be used for object detection in a weakly supervised manner. The Experimental results on ChestX-ray14 dataset 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.

这篇论文考虑建模图片之间的关系以生成更有信息量的图片表示，提出了ImageGCN，一个端到端的图卷积网络框架，可用于归纳式多关系图片建模，并应用于胸部X光图像进行疾病识别的实验，得到了优于基线方法和可与最先进方法相媲美甚至更好的定位和识别效果。

ImageGCN: 基于多关系图卷积网络的胸部X光疾病识别