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Scaling of Texture in Training Autoencoders for Classification of Histological Images of Colorectal Cancer
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
2017 (English)In: ADVANCES IN NEURAL NETWORKS, PT II, SPRINGER INTERNATIONAL PUBLISHING AG , 2017, Vol. 10262, p. 524-532Conference paper, Published paper (Refereed)
Abstract [en]

Autoencoding in deep learning has been known as a useful tool for extracting image features in multiple layers, which are subsequently configured for classification by deep neural networks. A practical burden for the implementation of autoencoders is the time required for training a large number of artificial neurons. This paper shows the effects of scaling of texture in the histology of colorectal cancer, which can result in significant training time reduction being approximately to an exponential function, with improved classification rates.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG , 2017. Vol. 10262, p. 524-532
Series
Lecture Notes in Computer Science, ISSN 0302-9743
Keywords [en]
Deep neural networks; Image classification; Digital pathology; Colorectal cancer; Tissue types
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-150325DOI: 10.1007/978-3-319-59081-3_61ISI: 000439964300061ISBN: 978-3-319-59081-3 (print)ISBN: 978-3-319-59080-6 (print)OAI: oai:DiVA.org:liu-150325DiVA, id: diva2:1239430
Conference
14th International Symposium on Neural Networks (ISNN)
Available from: 2018-08-16 Created: 2018-08-16 Last updated: 2018-08-16

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Pham, Tuan
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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf