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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.
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Chufan Gao, Stephen Clark, Jacob Furst, Daniela Raicu, "Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection," Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501K (13 March 2019); https://doi.org/10.1117/12.2513011