Cross-domain lung opacity detection via adversarial learning and box fusion.

Many conditions, such as pulmonary edema, bleeding, atelectasis or collapse, lung cancer, and shadow formation after radiotherapy or surgical changes, cause Lung Opacity. An unsupervised cross-domain Lung Opacity detection method is proposed to help surgeons quickly locate Lung Opacity without additional manual annotations. This study proposes a novel method based on adversarial learning to detect Lung Opacity on chest X-rays. Focal loss, GIoU loss, and WBF (weighted boxes fusion) were used in training. We conducted extensive experiments on Chest X-rays from RSNA (Radiological Society of North America) and Vingroup Big Data Institute to verify the performance of cross-domain detection. The results indicate that our method has superior performance. The AP reached 34.30% and 36.55%, while the AR reached 74.11% and 75.91% in two cross-domain detection tasks. The visualization results show that the randomly selected samples were more accurately detected for Lung Opacity after applying our method. Compared with other excellent detection frameworks, our method achieved competitive results without additional annotations, making it suitable for assisting in Lung Opacity detection.