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.
The authors propose a novel HEp‐2 cell image classifier to improve the automation process of patients' serum evaluation. The authors' solution builds on the recent progress in deep learning based image classification. They propose an ensemble approach using multiple state‐of‐the‐art architectures. They incorporate additional texture information extracted by an improved version of local binary patterns maps, αLBP‐maps, which enables to create a very effective cell image classifier. This innovative combination is trained on three publicly available datasets and its general applicability is demonstrated through the evaluation on three independent test sets. The presented results show that their approach leads to a general improvement of performance on average on the three public datasets.
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