MVC-NET: Multi-View Chest Radiograph Classification Network With Deep Fusion

Abstract

Chest radiography is a critical imaging modality to access thorax diseases. Automated radiograph classification algorithms have enormous potential to support clinical assistant diagnosis. Most algorithms focus solely on the single-view radiograph to make a prediction. However, both frontal and lateral images are valuable information sources for disease diagnosis. In this paper, we present multi-view chest radiograph classification network (MVC-Net) to fuse paired frontal and lateral views at both the feature and decision level. Specifically, back projection transposition(BPT) explicitly incorporates the spatial information from two orthogonal X-rays at feature level, and mimicry loss enables cross-view predictions to mimic from each other at decision level. The experimental results on 13 pathologies from MIMIC-CXR dataset show that MVC-Net yields the highest average AUROC score of 0.810, which gives better classification metrics as compared with various baseline methods. The code is available at https://github.com/fzfs/Multi-view-Chest-X-ray-Classification.