Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection
Chufan Gao, Stephen W. Clark, Jacob David Furst, Daniela Stan Raicu · Medical Imaging 2019: Computer-Aided Diagnosis · 2019
One drawback of Computer Aided Detection (CADe) systems is the large amount of data needed to train them, which may be expensive in the medical field. We propose using a generative adversarial network (GAN) as a potential data augmentation strategy to generate more training data to improve CADe. In our preliminary results, using the NIH/NCI Lung Image Database Consortium, we obtained a higher sensitivity when training a CADe system on our augmented lung nodule 3D data than training it without. We show that GANs are a viable method of data augmentation for lung nodule detection and are a promising area of potential research in the CADe domain.