One of the big challenges in the recognition of biomedical samples is the lack of large annotated datasets. Their relatively small size, when compared to datasets like ImageNet, typically leads to problems with ecient training of current machine learning algorithms. However, the recent development of generative adversarial networks (GANs) appears to be a step towards addressing this issue. In this study, we focus on one instance of GANs, which is known as deep convolutional generative adversarial network (DCGAN). It gained a lot of attention recently because of its stability in generating realistic articial images. Our article explores the possibilities of using DCGANs for generating HEp-2 images. We trained multiple DCGANs and generated several datasets of HEp-2 images. Subsequently, we combined them with traditional augmentation and evaluated over three dierent deep learning congurations. Our article demonstrates high visual quality of generated images, which is also supported by state-of-the-art classication results. Keywords: Deep learning • Image recognition • HEp-2 image classication • GAN • CNN • GoogLeNet • VGG-16 • Inception-v3 • Transfer learning.
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.