Abnormality Detection in Chest X-Ray via Residual-Saliency From Normal Generation

Abstract

In this paper, we propose a novel disease detection framework based on translating a given chest X-ray (CXR) to a corresponding normal CXR image. To train a model for normal CXR translation, we synthesize a paired image dataset from existing public CXR datasets. More specifically, we synthesize new images that appear to be from the same patient but without abnormalities, i.e., matching-pairs, that are not present in the dataset. Here, we search for normal local regions in images contained within the dataset that have a similar appearance, and we apply a blending process to effectively erase the abnormal regions. A conditional GAN can then be trained on the synthetic matching-pair images for a normal CXR translator model that transforms the given CXR images into their corresponding normal CXRs. We incorporate this pretrained translator model into a deep learning based object detection model. Here we define the difference between the translated normal CXR and the given input as the residual-saliency map. As the residual-saliency map will be most active at diseased regions, we use this as the attention within the detection model. We also utilize the matching-pair synthesis pipeline to synthesize abnormal images for data augmentation when we train the detector. We evaluate the proposed approach on two public chest X-ray datasets: RSNA and VinBigData.

Keywords

• Abnormality detection
• normal generation
• matching pair synthesis
• chest X-Ray
• deep learning