Abstract: self-supervised 3d context feature learning on unlabeled volume data

Maximilian Blendowski; Mattias P. Heinrich

doi:10.1007/978-3-658-29267-6_41

Abstract: self-supervised 3d context feature learning on unlabeled volume data

Maximilian Blendowski^*, Mattias P. Heinrich

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Medizinische Informatik

Abstract

Deep learning with convolutional networks (DCNN) has established itself as a powerful tool for a variety of medical imaging tasks. However, DCNNs in particular require strong monitoring by expert annotations, which cannot be generated cost-effectively by laymen. In contrast to manual annotations, the mere availability of medical volume data is not a problem.

Originalsprache	Englisch
Titel	Bildverarbeitung für die Medizin 2020
Redakteure/-innen	Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm
Seitenumfang	1
Herausgeber (Verlag)	Springer Vieweg, Wiesbaden
Erscheinungsdatum	12.02.2020
Seiten	192-192
ISBN (Print)	978-3-658-29266-9
ISBN (elektronisch)	978-3-658-29267-6
DOIs	https://doi.org/10.1007/978-3-658-29267-6_41
Publikationsstatus	Veröffentlicht - 12.02.2020
Veranstaltung	Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen - Berlin, Deutschland Dauer: 15.03.2020 → 17.03.2020 Konferenznummer: 237969

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@inproceedings{b3552090fa7a40e3814c057698d32e5d,

title = "Abstract: self-supervised 3d context feature learning on unlabeled volume data",

abstract = "Deep learning with convolutional networks (DCNN) has established itself as a powerful tool for a variety of medical imaging tasks. However, DCNNs in particular require strong monitoring by expert annotations, which cannot be generated cost-effectively by laymen. In contrast to manual annotations, the mere availability of medical volume data is not a problem.",

author = "Maximilian Blendowski and Heinrich, {Mattias P.}",

note = "Funding Information: Acknowledgement. This work was supported by the German Research Foundation (DFG) under grant number 320997906 (HE 7364/2-1) and by the NVIDIA Corporation with their GPU donations. Find our code here: 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",

year = "2020",

month = feb,

day = "12",

doi = "10.1007/978-3-658-29267-6_41",

language = "English",

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

series = "Informatik aktuell",

publisher = "Springer Vieweg, Wiesbaden",

pages = "192--192",

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

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2020",

}

Blendowski, M & Heinrich, MP 2020, Abstract: self-supervised 3d context feature learning on unlabeled volume data. in T Tolxdorff, T M. Deserno, H Handels, A Maier, K H. Maier-Hein & C Palm (Hrsg.), Bildverarbeitung für die Medizin 2020. Informatik aktuell, Springer Vieweg, Wiesbaden, S. 192-192, Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen
, Berlin, Berlin, Deutschland, 15.03.20. https://doi.org/10.1007/978-3-658-29267-6_41

Abstract: self-supervised 3d context feature learning on unlabeled volume data. / Blendowski, Maximilian; Heinrich, Mattias P.
Bildverarbeitung für die Medizin 2020. Hrsg. / Thomas Tolxdorff; Thomas M. Deserno; Heinz Handels; Andreas Maier; Klaus H. Maier-Hein; Christoph Palm. Springer Vieweg, Wiesbaden, 2020. S. 192-192 (Informatik aktuell).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Abstract: self-supervised 3d context feature learning on unlabeled volume data

AU - Blendowski, Maximilian

AU - Heinrich, Mattias P.

N1 - Conference code: 237969

PY - 2020/2/12

Y1 - 2020/2/12

N2 - Deep learning with convolutional networks (DCNN) has established itself as a powerful tool for a variety of medical imaging tasks. However, DCNNs in particular require strong monitoring by expert annotations, which cannot be generated cost-effectively by laymen. In contrast to manual annotations, the mere availability of medical volume data is not a problem.

AB - Deep learning with convolutional networks (DCNN) has established itself as a powerful tool for a variety of medical imaging tasks. However, DCNNs in particular require strong monitoring by expert annotations, which cannot be generated cost-effectively by laymen. In contrast to manual annotations, the mere availability of medical volume data is not a problem.

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DO - 10.1007/978-3-658-29267-6_41

M3 - Conference contribution

SN - 978-3-658-29266-9

T3 - Informatik aktuell

SP - 192

EP - 192

BT - Bildverarbeitung für die Medizin 2020

A2 - Tolxdorff, Thomas

A2 - M. Deserno, Thomas

A2 - Handels, Heinz

A2 - Maier, Andreas

A2 - H. Maier-Hein, Klaus

A2 - Palm, Christoph

PB - Springer Vieweg, Wiesbaden

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

Y2 - 15 March 2020 through 17 March 2020

ER -

Abstract: self-supervised 3d context feature learning on unlabeled volume data

Abstract

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