On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool

Tomas Majtner*, Buda Bajic, J Lindblad, N Sladoje, Victoria Blanes-Vidal, Esmaeil S. Nadimi

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Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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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.
OriginalsprogEngelsk
TitelImage Analysis : Proceedings of the 21st Scandinavian Conference, SCIA 2019
RedaktørerMichael Felsberg, Per-Erik Forssén, Ida-Maria Sintorn, Jonas Unger
ForlagSpringer
Publikationsdato2019
Sider439-451
ISBN (Trykt)978-3-030-20204-0
ISBN (Elektronisk)978-3-030-20205-7
DOI
StatusUdgivet - 2019
Begivenhed21st Scandinavian Conference on Image Analysis, SCIA 2019 - Norrköping, Sverige
Varighed: 11. jun. 201913. jun. 2019

Konference

Konference21st Scandinavian Conference on Image Analysis, SCIA 2019
Land/OmrådeSverige
ByNorrköping
Periode11/06/201913/06/2019
NavnLecture Notes in Computer Science
Vol/bind11482
ISSN0302-9743
NavnImage processing, computer vision, pattern recognition and graphics

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