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  • Källén, HannaLund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,Centre for Mathematical Sciences, Lund University, Sweden (author)

Towards Grading Gleason Score using Generically Trained Deep convolutional Neural Networks

  • Article/chapterEnglish2016

Publisher, publication year, extent ...

  • Institute of Electrical and Electronics Engineers (IEEE),2016
  • 5 s.
  • electronicrdacarrier

Numbers

  • LIBRIS-ID:oai:lup.lub.lu.se:839c04b4-164c-4145-8620-9a6f2e5139c4
  • ISBN:9781479923502
  • https://lup.lub.lu.se/record/839c04b4-164c-4145-8620-9a6f2e5139c4URI
  • https://doi.org/10.1109/ISBI.2016.7493473DOI
  • https://research.chalmers.se/publication/244398URI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132877URI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:kon swepub-publicationtype
  • Subject category:ref swepub-contenttype

Notes

  • 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 3-5. Using 10-fold cross validation the accuracy per patch was 81 %. For whole images, the accuracy was increased to 89 %.

Subject headings and genre

Added entries (persons, corporate bodies, meetings, titles ...)

  • Molin, Jesper,1987Linköping University,Chalmers University of Technology,t2iLab, Chalmers University of Technology, Sweden; Sectra AB, Linköping, Sweden,Linköpings universitet,Chalmers tekniska högskola,Centrum för medicinsk bildvetenskap och visualisering, CMIV(Swepub:cth)mjesper (author)
  • Heyden, AndersLund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Teknisk matematik (CI),Utbildningsprogram, LTH,Lunds Tekniska Högskola,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lund University Research Groups,Engineering Mathematics (M.Sc.Eng.),Educational programmes, LTH,Faculty of Engineering, LTH,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,Centre for Mathematical Sciences, Lund University, Sweden(Swepub:lu)math-ahe (author)
  • Lundström, ClaesLinköping University,Sectra AB, Linköping, Sweden,Linköpings universitet,Medie- och Informationsteknik,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV(Swepub:liu)clalu03 (author)
  • Åström, KarlLund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH(Swepub:lu)math-kas (author)
  • Åström, KalleCentre for Mathematical Sciences, Lund University, Sweden (author)
  • Mathematical Imaging GroupForskargrupper vid Lunds universitet (creator_code:org_t)

Related titles

  • In:2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI): Institute of Electrical and Electronics Engineers (IEEE)2016-June, s. 1163-116797814799234969781479923502
  • In:Proceedings - International Symposium on Biomedical Imaging: Institute of Electrical and Electronics Engineers (IEEE)2016-June, s. 1163-11671945-84529781479923502

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