Generative Adversarial Networks to Synthetically Augment Data for Deep Learning based Image Segmentation

Thomas Neff; Christian Payer; Darko Štern; Martin Urschler

doi:10.3217/978-3-85125-603-1-07

Generative Adversarial Networks to Synthetically Augment Data for Deep Learning based Image Segmentation

Thomas Neff, Christian Payer, Darko Štern, Martin Urschler

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Original language	English
Title of host publication	Proceedings of the OAGM Workshop 2018
Subtitle of host publication	Medical Image Analysis
Editors	Martin Welk, Peter M. Roth, Martin Urschler
Publisher	Verlag der Technischen Universität Graz
Pages	22-29
Number of pages	8
ISBN (Electronic)	978-3-85125-603-1
DOIs	https://doi.org/10.3217/978-3-85125-603-1-07
Publication status	Published - May 2018
Event	42nd Annual Workshop of the Austrian Association for Pattern Recognition: Medical Image Analysis: ÖAGM 2018 - Hall/Tirol, Austria Duration: 15 May 2018 → 16 May 2018

Workshop

Workshop	42nd Annual Workshop of the Austrian Association for Pattern Recognition: Medical Image Analysis
Country/Territory	Austria
City	Hall/Tirol
Period	15/05/18 → 16/05/18

Fields of Expertise

Information, Communication & Computing

Cooperations

BioTechMed-Graz

Access to Document

10.3217/978-3-85125-603-1-07Licence: CC BY 4.0

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents
Bischof, H. & Urschler, M.
1/07/15 → 31/12/18
Project: Research project

Cite this

Generative Adversarial Networks to Synthetically Augment Data for Deep Learning based Image Segmentation. / Neff, Thomas; Payer, Christian; Štern, Darko et al.
Proceedings of the OAGM Workshop 2018: Medical Image Analysis. ed. / Martin Welk; Peter M. Roth; Martin Urschler. Verlag der Technischen Universität Graz, 2018. p. 22-29.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Neff, T, Payer, C, Štern, D & Urschler, M 2018, Generative Adversarial Networks to Synthetically Augment Data for Deep Learning based Image Segmentation. in M Welk, PM Roth & M Urschler (eds), Proceedings of the OAGM Workshop 2018: Medical Image Analysis. Verlag der Technischen Universität Graz, pp. 22-29, 42nd Annual Workshop of the Austrian Association for Pattern Recognition: Medical Image Analysis, Hall/Tirol, Austria, 15/05/18. https://doi.org/10.3217/978-3-85125-603-1-07

@inproceedings{8b57cc2d76ae40f4a25f78579f8222c2,

title = "Generative Adversarial Networks to Synthetically Augment Data for Deep Learning based Image Segmentation",

author = "Thomas Neff and Christian Payer and Darko {\v S}tern and Martin Urschler",

year = "2018",

month = may,

doi = "10.3217/978-3-85125-603-1-07",

language = "English",

pages = "22--29",

editor = "Martin Welk and Roth, {Peter M.} and Martin Urschler",

booktitle = "Proceedings of the OAGM Workshop 2018",

publisher = "Verlag der Technischen Universit{\"a}t Graz",

note = "42nd Annual Workshop of the Austrian Association for Pattern Recognition: Medical Image Analysis : {\"O}AGM 2018 ; Conference date: 15-05-2018 Through 16-05-2018",

}

TY - GEN

T1 - Generative Adversarial Networks to Synthetically Augment Data for Deep Learning based Image Segmentation

AU - Neff, Thomas

AU - Payer, Christian

AU - Štern, Darko

AU - Urschler, Martin

PY - 2018/5

Y1 - 2018/5

U2 - 10.3217/978-3-85125-603-1-07

DO - 10.3217/978-3-85125-603-1-07

M3 - Conference paper

SP - 22

EP - 29

BT - Proceedings of the OAGM Workshop 2018

A2 - Welk, Martin

A2 - Roth, Peter M.

A2 - Urschler, Martin

PB - Verlag der Technischen Universität Graz

T2 - 42nd Annual Workshop of the Austrian Association for Pattern Recognition: Medical Image Analysis

Y2 - 15 May 2018 through 16 May 2018

ER -

Generative Adversarial Networks to Synthetically Augment Data for Deep Learning based Image Segmentation

Workshop

Fields of Expertise

Cooperations

Access to Document

Projects

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents

Cite this