This work aims to fight against the current outbreak pandemic by developing a method to classify suspected infected COVID-19 cases. Driven by the urgency, due to the vastly increased number of patients and deaths worldwide, we rely on situationally pragmatic chest X-ray scans and state-ofthe-art deep learning techniques to build a robust diagnosis for massive screening, early detection, and in-time isolation decision making. The proposed solution, X-ray Projected Generative Adversarial Network (XPGAN), addresses the most fundamental issue in training such a deep neural network on limited human-annotated datasets. By leveraging the generative adversarial network, we can synthesize a large amount of chest X-ray images with prior categories from more accurate 3D Computed Tomography data, including COVID-19, and jointly train a model with a few hundreds of positive samples. As a result, XPGAN outperforms the vanilla DenseNet121 models and other competing baselines trained on the same frontal chest X-ray images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.