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

Research Areas and Centers

Academic Focus: Biomedical Engineering

UN SDGs

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

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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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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",

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publisher = "Springer Vieweg, Wiesbaden",

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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

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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.

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BT - Bildverarbeitung für die Medizin 2020

A2 - Tolxdorff, Thomas

A2 - Deserno, Thomas M.

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A2 - Maier-Hein, Klaus H.

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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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