Lung Segmentation Reconstruction Based Data Augmentation Approach for Abnormal Chest X-ray Images Diagnosis.

Experienced radiologists can accurately diagnose relevant diseases by observing the cardiopulmonary region in chest X-ray images. Advances in deep learning techniques enable the prediction of lesions in chest X-ray images. However, deep learning-based algorithms usually require a large amount of training data, and it lacks an effective method for data generation and augmentation. In this paper, we propose a Lung Segmentation Reconstruction (LSR) module. A healthy chest X-ray image is generated based on the abnormal image as a reference. With the generated healthy reference, we propose a novel way of data augmentation for chest X-ray images. The whole images, lung regions and abnormal regions are stacked together and fed into a classification model to make a credible diagnosis. Extensive experiments have been conducted on the public dataset CheXpert. Experimental results show that our proposed abnormality enhancement can help the baseline models achieve better performance on consolidation and pleural effusion. These results highlight the potential value of the large number of healthy chest X-ray images in the dataset and the combination of different regions of chest X-ray images for prediction.