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Authors: Filippos Konidaris ; Thanos Tagaris ; Maria Sdraka and Andreas Stafylopatis

Affiliation: School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytexneiou 9, Zografou Campus 15780, Athens, Greece

ISBN: 978-989-758-354-4

Keyword(s): Generative Adversarial Networks, Deep Learning, MRI, Data Augmentation, ADNI, Alzheimer’s Disease.

Abstract: This paper presents a new methodology for data augmentation through the use of Generative Adversarial Networks. Traditional augmentation strategies are severely limited, especially in tasks where the images follow strict standards, as is the case in medical datasets. Experiments conducted on the ADNI dataset prove that augmentation through GANs outperforms traditional methods by a large margin, based both on the validation accuracy and the models’ generalization capability on a holdout test set. Although traditional data augmentation did not seem to aid the classification process in any way, by adding GAN-based augmentation an increase of 11.68% in accuracy was achieved. Furthermore, by combining traditional with GAN-based augmentation schemes, even higher accuracies can be reached.

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Paper citation in several formats:
Konidaris, F.; Tagaris, T.; Sdraka, M. and Stafylopatis, A. (2019). Generative Adversarial Networks as an Advanced Data Augmentation Technique for MRI Data.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 48-59. DOI: 10.5220/0007363900480059

@conference{visapp19,
author={Filippos Konidaris. and Thanos Tagaris. and Maria Sdraka. and Andreas Stafylopatis.},
title={Generative Adversarial Networks as an Advanced Data Augmentation Technique for MRI Data},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={48-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007363900480059},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Generative Adversarial Networks as an Advanced Data Augmentation Technique for MRI Data
SN - 978-989-758-354-4
AU - Konidaris, F.
AU - Tagaris, T.
AU - Sdraka, M.
AU - Stafylopatis, A.
PY - 2019
SP - 48
EP - 59
DO - 10.5220/0007363900480059

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