Generation of synthetic ground glass opacities (GGOs) using generative adversarial networks (GANs)

The robustness of deep learning (DL)-based computer aided diagnosis (CAD) systems for the automated detection of pulmonary nodules in low-dose CT scans strongly relies on the availability of a large amount of curated and annotated data. Even when this holds true, the unbalance problem will exist. For example, GGOs will be harder to be recognized by the algorithm, because of their lower prevalence compared to part solid and solid nodules. DL algorithms such as GANs can be utilized to generate synthetic samples, thus increasing the original datasets and improve the CAD detection accuracy.