Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images

Hristina Uzunova; Jan Ehrhardt; Fabian Jacob; Alex Frydrychowicz; Heinz Handels

doi:10.1007/978-3-658-29267-6_63

Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images

Hristina Uzunova^*, Jan Ehrhardt, Fabian Jacob, Alex Frydrychowicz, Heinz Handels

^*Corresponding author for this work

Abstract

Generative adversarial networks (GANs) have shown impressive results for photo-realistic image synthesis in the last couple of years. They also offer numerous applications in medical image analysis, such as generating images for data augmentation, image reconstruction and image synthesis for domain adaptation. Despite the undeniable success and the large variety of applications, GANs still struggle to generate images of high resolution.

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2020
Editors	Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein
Number of pages	1
Place of Publication	Wiesbaden
Publisher	Springer Vieweg, Wiesbaden
Publication date	12.02.2020
Pages	286-286
ISBN (Print)	978-3-658-29266-9
ISBN (Electronic)	978-3-658-29267-6
DOIs	https://doi.org/10.1007/978-3-658-29267-6_63
Publication status	Published - 12.02.2020
Event	Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen - Berlin, Germany Duration: 15.03.2020 → 17.03.2020 Conference number: 237969

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

Research Areas and Centers

Academic Focus: Biomedical Engineering

Access to Document

10.1007/978-3-658-29267-6_63

Cite this

Uzunova, H., Ehrhardt, J., Jacob, F., Frydrychowicz, A., & Handels, H. (2020). Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images. In T. Tolxdorff, T. M. Deserno, H. Handels, A. Maier, & K. H. Maier-Hein (Eds.), Bildverarbeitung für die Medizin 2020 (pp. 286-286). (Informatik aktuell). Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_63

Uzunova, Hristina ; Ehrhardt, Jan ; Jacob, Fabian et al. / Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images. Bildverarbeitung für die Medizin 2020. editor / Thomas Tolxdorff ; Thomas M. Deserno ; Heinz Handels ; Andreas Maier ; Klaus H. Maier-Hein. Wiesbaden : Springer Vieweg, Wiesbaden, 2020. pp. 286-286 (Informatik aktuell).

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title = "Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images",

abstract = "Generative adversarial networks (GANs) have shown impressive results for photo-realistic image synthesis in the last couple of years. They also offer numerous applications in medical image analysis, such as generating images for data augmentation, image reconstruction and image synthesis for domain adaptation. Despite the undeniable success and the large variety of applications, GANs still struggle to generate images of high resolution.",

author = "Hristina Uzunova and Jan Ehrhardt and Fabian Jacob and Alex Frydrychowicz and Heinz Handels",

note = "Publisher Copyright: {\textcopyright} Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Bildverarbeitung f{\"u}r die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen<br/> ; Conference date: 15-03-2020 Through 17-03-2020",

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doi = "10.1007/978-3-658-29267-6\_63",

language = "English",

isbn = "978-3-658-29266-9",

series = "Informatik aktuell",

publisher = "Springer Vieweg, Wiesbaden",

pages = "286--286",

editor = "Tolxdorff, \{Thomas \} and Deserno, \{Thomas M. \} and Handels, \{Heinz \} and Maier, \{Andreas \} and Maier-Hein, \{Klaus H. \}",

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Uzunova, H , Ehrhardt, J , Jacob, F , Frydrychowicz, A & Handels, H 2020, Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images. in T Tolxdorff, TM Deserno, H Handels, A Maier & KH Maier-Hein (eds), Bildverarbeitung für die Medizin 2020. Informatik aktuell, Springer Vieweg, Wiesbaden, Wiesbaden, pp. 286-286, Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen
, Berlin, Berlin, Germany, 15.03.20. https://doi.org/10.1007/978-3-658-29267-6_63

Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images. / Uzunova, Hristina ; Ehrhardt, Jan ; Jacob, Fabian et al.
Bildverarbeitung für die Medizin 2020. ed. / Thomas Tolxdorff; Thomas M. Deserno; Heinz Handels; Andreas Maier; Klaus H. Maier-Hein. Wiesbaden: Springer Vieweg, Wiesbaden, 2020. p. 286-286 (Informatik aktuell).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

TY - GEN

T1 - Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images

AU - Uzunova, Hristina

AU - Ehrhardt, Jan

AU - Jacob, Fabian

AU - Frydrychowicz, Alex

AU - Handels, Heinz

N1 - Conference code: 237969

PY - 2020/2/12

Y1 - 2020/2/12

N2 - Generative adversarial networks (GANs) have shown impressive results for photo-realistic image synthesis in the last couple of years. They also offer numerous applications in medical image analysis, such as generating images for data augmentation, image reconstruction and image synthesis for domain adaptation. Despite the undeniable success and the large variety of applications, GANs still struggle to generate images of high resolution.

AB - Generative adversarial networks (GANs) have shown impressive results for photo-realistic image synthesis in the last couple of years. They also offer numerous applications in medical image analysis, such as generating images for data augmentation, image reconstruction and image synthesis for domain adaptation. Despite the undeniable success and the large variety of applications, GANs still struggle to generate images of high resolution.

UR - https://www.scopus.com/pages/publications/85083077278

UR - https://www.mendeley.com/catalogue/25df6c1d-7207-3ece-8afc-7994dc1c162e/

U2 - 10.1007/978-3-658-29267-6_63

DO - 10.1007/978-3-658-29267-6_63

M3 - Conference contribution

SN - 978-3-658-29266-9

T3 - Informatik aktuell

SP - 286

EP - 286

BT - Bildverarbeitung für die Medizin 2020

A2 - Tolxdorff, Thomas

A2 - Deserno, Thomas M.

A2 - Handels, Heinz

A2 - Maier, Andreas

A2 - Maier-Hein, Klaus H.

PB - Springer Vieweg, Wiesbaden

CY - Wiesbaden

T2 - Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen<br/>

Y2 - 15 March 2020 through 17 March 2020

ER -

Uzunova H , Ehrhardt J , Jacob F , Frydrychowicz A , Handels H. Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images. In Tolxdorff T, Deserno TM, Handels H, Maier A, Maier-Hein KH, editors, Bildverarbeitung für die Medizin 2020. Wiesbaden: Springer Vieweg, Wiesbaden. 2020. p. 286-286. (Informatik aktuell). doi: 10.1007/978-3-658-29267-6_63

Abstract: Multi-Scale GANs for Memory-Effcient Generation of High Resolution Medical Images

Abstract

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