Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision. However, most existing deep learning models only look at the entire X-ray image for classification, failing to utilize important anatomical information. In this paper, we propose a novel multi-label chest X-ray classification model that accurately classifies the image finding and also localizes the findings to their correct anatomical regions. Specifically, our model consists of two modules, the detection module and the anatomical dependency module. The latter utilizes graph convolutional networks, which enable our model to learn not only the label dependency but also the relationship between the anatomical regions in the chest X-ray. We further utilize a method to efficiently create an adjacency matrix for the anatomical regions using the correlation of the label across the different regions. Detailed experiments and analysis of our results show the effectiveness of our method when compared to the current state-of-the-art multi-label chest X-ray image classification methods while also providing accurate location information.

本文提出了一种多标签胸部 X 光分类模型，能够准确分类图像发现，同时将发现位置定位到正确的解剖区域。该模型包括两个模块 - 检测模块和解剖依赖模块，并采用图卷积网络实现解剖部位之间的有关系学习。实验结果表明，与现有的多标签胸部 X 射线图像分类方法相比，该方法是有效且能够提供精确的位置信息。

AnaXNet：基于解剖学的胸部X光多标签病变定位分类