liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
Towards grading gleason score using generically trained deep convolutional neural networks
Centre for Mathematical Sciences, Lund University, Sweden.
Linköping University, Center for Medical Image Science and Visualization (CMIV). t2iLab, Chalmers University of Technology, Sweden; Sectra AB, Linköping, Sweden.
Centre for Mathematical Sciences, Lund University, Sweden.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra AB, Linköping, Sweden.ORCID iD: 0000-0002-9368-0177
Show others and affiliations
2016 (English)In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Institute of Electrical and Electronics Engineers (IEEE), 2016, 1163-1167 p.Conference paper, Published paper (Refereed)
Abstract [en]

We developed an automatic algorithm with the purpose to assist pathologists to report Gleason score on malignant prostatic adenocarcinoma specimen. In order to detect and classify the cancerous tissue, a deep convolutional neural network that had been pre-trained on a large set of photographic images was used. A specific aim was to support intuitive interaction with the result, to let pathologists adjust and correct the output. Therefore, we have designed an algorithm that makes a spatial classification of the whole slide into the same growth patterns as pathologists do. The 22-layer network was cut at an earlier layer and the output from that layer was used to train both a random forest classifier and a support vector machines classifier. At a specific layer a small patch of the image was used to calculate a feature vector and an image is represented by a number of those vectors. We have classified both the individual patches and the entire images. The classification results were compared for different scales of the images and feature vectors from two different layers from the network. Testing was made on a dataset consisting of 213 images, all containing a single class, benign tissue or Gleason score 35. Using 10-fold cross validation the accuracy per patch was 81 %. For whole images, the accuracy was increased to 89 %.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 1163-1167 p.
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keyword [en]
Prostate cancer, Gleason Score, Deep Learning, Convolutional Neural Networks
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-132877DOI: 10.1109/ISBI.2016.7493473ISI: 000386377400275ISBN: 978-1-4799-2349-6 (print)ISBN: 978-1-4799-2350-2 (print)OAI: oai:DiVA.org:liu-132877DiVA: diva2:1052314
Conference
IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, April 13-16, 2016
Available from: 2016-12-06 Created: 2016-11-30 Last updated: 2016-12-13Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Lundström, Claes
By organisation
Center for Medical Image Science and Visualization (CMIV)Media and Information TechnologyFaculty of Science & Engineering
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 48 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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