Exploring sparsity in CNNs for medical image segmentation BRIEFnet

Mattias P. Heinrich; Ozan Oktay

doi:10.1007/978-3-662-56537-7_25

Exploring sparsity in CNNs for medical image segmentation BRIEFnet

Mattias P. Heinrich^*, Ozan Oktay

^*Corresponding author for this work

Institute of Medical Informatics

Abstract

Deep convolutional neural networks can evidently achieve astonishing accuracies for multiple medical image analysis tasks, in particular segmentation and detection. However, the actual translation of deep learning into clinical practice is so far very limited, in part because their extensive computations rely on specialised GPU hardware that is not easily available in clinical environments.

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2018
Editors	Andreas Maier, Thomas Deserno, Heinz Handels, Klaus Hermann Maier-Hein, Christoph Palm, Thomas Tolxdorff
Number of pages	2
Volume	1
Publisher	Springer Verlag
Publication date	01.01.2018
Edition	211279
Pages	40-41
ISBN (Print)	978-3-662-56536-0
ISBN (Electronic)	978-3-662-56537-7
DOIs	https://doi.org/10.1007/978-3-662-56537-7_25
Publication status	Published - 01.01.2018
Event	Bildverarbeitung für die Medizin 2018 - Lehrstuhl für Mustererkennung, Erlangen, Germany Duration: 11.03.2018 → 13.03.2018 https://www.springer.com/us/book/9783662565360 http://www.bvm-workshop.org

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10.1007/978-3-662-56537-7_25

https://www.springer.com/de/book/9783662565360

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@inproceedings{bb346512341141cc876af33e04efeda5,

title = "Exploring sparsity in CNNs for medical image segmentation BRIEFnet",

abstract = "Deep convolutional neural networks can evidently achieve astonishing accuracies for multiple medical image analysis tasks, in particular segmentation and detection. However, the actual translation of deep learning into clinical practice is so far very limited, in part because their extensive computations rely on specialised GPU hardware that is not easily available in clinical environments.",

author = "Heinrich, {Mattias P.} and Ozan Oktay",

year = "2018",

month = jan,

day = "1",

doi = "10.1007/978-3-662-56537-7_25",

language = "English",

isbn = "978-3-662-56536-0",

volume = "1",

series = "Informatik aktuell",

publisher = "Springer Verlag",

pages = "40--41",

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

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2018",

edition = "211279",

note = "Bildverarbeitung f{\"u}r die Medizin 2018 ; Conference date: 11-03-2018 Through 13-03-2018",

url = "https://www.springer.com/us/book/9783662565360, http://www.bvm-workshop.org",

}

Heinrich, MP & Oktay, O 2018, Exploring sparsity in CNNs for medical image segmentation BRIEFnet. in A Maier, T Deserno, H Handels, KH Maier-Hein, C Palm & T Tolxdorff (eds), Bildverarbeitung für die Medizin 2018. 211279 edn, vol. 1, Informatik aktuell, Springer Verlag, pp. 40-41, Bildverarbeitung für die Medizin 2018, Erlangen, Germany, 11.03.18. https://doi.org/10.1007/978-3-662-56537-7_25

Exploring sparsity in CNNs for medical image segmentation BRIEFnet. / Heinrich, Mattias P.; Oktay, Ozan.
Bildverarbeitung für die Medizin 2018. ed. / Andreas Maier; Thomas Deserno; Heinz Handels; Klaus Hermann Maier-Hein; Christoph Palm; Thomas Tolxdorff. Vol. 1 211279. ed. Springer Verlag, 2018. p. 40-41 (Informatik aktuell).

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

TY - GEN

T1 - Exploring sparsity in CNNs for medical image segmentation BRIEFnet

AU - Heinrich, Mattias P.

AU - Oktay, Ozan

PY - 2018/1/1

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N2 - Deep convolutional neural networks can evidently achieve astonishing accuracies for multiple medical image analysis tasks, in particular segmentation and detection. However, the actual translation of deep learning into clinical practice is so far very limited, in part because their extensive computations rely on specialised GPU hardware that is not easily available in clinical environments.

AB - Deep convolutional neural networks can evidently achieve astonishing accuracies for multiple medical image analysis tasks, in particular segmentation and detection. However, the actual translation of deep learning into clinical practice is so far very limited, in part because their extensive computations rely on specialised GPU hardware that is not easily available in clinical environments.

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DO - 10.1007/978-3-662-56537-7_25

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AN - SCOPUS:85043342310

SN - 978-3-662-56536-0

VL - 1

T3 - Informatik aktuell

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BT - Bildverarbeitung für die Medizin 2018

A2 - Maier, Andreas

A2 - Deserno, Thomas

A2 - Handels, Heinz

A2 - Maier-Hein, Klaus Hermann

A2 - Palm, Christoph

A2 - Tolxdorff, Thomas

PB - Springer Verlag

T2 - Bildverarbeitung für die Medizin 2018

Y2 - 11 March 2018 through 13 March 2018

ER -

Exploring sparsity in CNNs for medical image segmentation BRIEFnet

Abstract

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