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On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool
The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Denmark. (Group of Machine Learning and AI)
Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. (MIDA)ORCID iD: 0000-0001-7312-8222
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. (MIDA)ORCID iD: 0000-0002-6041-6310
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2019 (English)In: Scandinavian Conference on Image Analysis: SCIA 2019 / [ed] Michael Felsberg, Per-Erik Forssén, Ida-Maria Sintorn, Jonas Unger, 2019, p. 439-451Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. p. 439-451
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11482
Keywords [en]
Deep learning, Image recognition, HEp-2 image classification, GAN, CNN, GoogLeNet, VGG-16, Inception-v3, Transfer learning
National Category
Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-398231DOI: 10.1007/978-3-030-20205-7_36ISBN: 978-3-030-20204-0 (print)OAI: oai:DiVA.org:uu-398231DiVA, id: diva2:1375059
Conference
Scandinavian Conference on Image Analysis
Available from: 2019-12-03 Created: 2019-12-03 Last updated: 2019-12-03

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Publisher's full texthttps://doi.org/10.1007/978-3-030-20205-7_36

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Lindblad, JoakimSladoje, Natasa

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Computerized Image Analysis and Human-Computer InteractionDivision of Visual Information and Interaction
Computer Vision and Robotics (Autonomous Systems)Medical Image Processing

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