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

^*Corresponding author for this work

Institute of Medical Informatics

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.

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, Christoph Palm
Number of pages	1
Publisher	Springer Vieweg, Wiesbaden
Publication date	12.02.2020
Pages	192-192
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_41
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

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

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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 (eds), Bildverarbeitung für die Medizin 2020. Informatik aktuell, Springer Vieweg, Wiesbaden, pp. 192-192, 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_41

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

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

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AU - Blendowski, Maximilian

AU - Heinrich, Mattias P.

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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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BT - Bildverarbeitung für die Medizin 2020

A2 - Tolxdorff, Thomas

A2 - M. Deserno, Thomas

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A2 - Maier, Andreas

A2 - H. Maier-Hein, Klaus

A2 - Palm, Christoph

PB - Springer Vieweg, Wiesbaden

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ER -

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

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