Cited By
View all- Majtner T(2021)HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic SamplesComputer Analysis of Images and Patterns10.1007/978-3-030-89128-2_21(215-225)Online publication date: 28-Sep-2021
On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool
Authors: 
Tomáš Majtner, Buda Bajić, Joakim Lindblad, Nataša Sladoje, Victoria Blanes-Vidal, Esmaeil S. NadimiAuthors Info & Claims
Pages 439 - 451
Abstract
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 efficient 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 convolutio nal generative adversarial network (DCGAN). It gained a lot of attention recently because of its stability in generating realistic artificial 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 different deep learning configurations. Our article demonstrates high visual quality of generated images, which is also supported by state-of-the-art classification results.
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Published In
Jun 2019
507 pages
ISBN:978-3-030-20204-0
DOI:10.1007/978-3-030-20205-7
© Springer Nature Switzerland AG 2019.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 11 June 2019
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View all- Majtner T(2021)HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic SamplesComputer Analysis of Images and Patterns10.1007/978-3-030-89128-2_21(215-225)Online publication date: 28-Sep-2021