Mitosis detection in breast cancer histology images with deep neural networks

Dan C. Cireşan; Alessandro Giusti; Luca M. Gambardella; Jürgen Schmidhuber

doi:10.1007/978-3-642-40763-5_51

Mitosis detection in breast cancer histology images with deep neural networks

Dan C. Cireşan, Alessandro Giusti, Luca M. Gambardella, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1286 Scopus citations

Abstract

We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin. © 2013 Springer-Verlag.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	411-418
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-40763-5_51
State	Published - Oct 24 2013
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

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

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10.1007/978-3-642-40763-5_51

Cite this

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title = "Mitosis detection in breast cancer histology images with deep neural networks",

abstract = "We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin. {\textcopyright} 2013 Springer-Verlag.",

author = "Cire{\c s}an, {Dan C.} and Alessandro Giusti and Gambardella, {Luca M.} and J{\"u}rgen Schmidhuber",

note = "Generated from Scopus record by KAUST IRTS on 2022-09-14",

year = "2013",

month = oct,

day = "24",

doi = "10.1007/978-3-642-40763-5_51",

language = "English (US)",

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Mitosis detection in breast cancer histology images with deep neural networks. / Cireşan, Dan C.; Giusti, Alessandro; Gambardella, Luca M. et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2013. p. 411-418.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Mitosis detection in breast cancer histology images with deep neural networks

AU - Cireşan, Dan C.

AU - Giusti, Alessandro

AU - Gambardella, Luca M.

AU - Schmidhuber, Jürgen

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14

PY - 2013/10/24

Y1 - 2013/10/24

N2 - We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin. © 2013 Springer-Verlag.

AB - We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin. © 2013 Springer-Verlag.

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DO - 10.1007/978-3-642-40763-5_51

M3 - Conference contribution

SN - 9783642407628

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EP - 418

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Mitosis detection in breast cancer histology images with deep neural networks

Abstract

Bibliographical note

ASJC Scopus subject areas

UN SDGs

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