Generalization of prostate cancer classification for multiple sites using deep learning

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

Abstract

Deep learning has the potential to drastically increase the accuracy and efficiency of prostate cancer diagnosis, which would be of uttermost use. Today the diagnosis is determined manually from H&E stained specimens using a light microscope. In this paper several different approaches based on convolutional neural networks for prostate cancer classification are presented and compared, using three different datasets with different origins. The issue that algorithms trained on a certain site might not generalize to other sites, due to for example inevitable stain variations, is highlighted. Two different techniques to overcome this complication are compared; by training the networks using color augmentation and by using digital stain separation. Furthermore, the potential of using an autoencoder to get a more efficient downsampling is investigated, which turned out to be the method giving the best generalization. We achieve accuracies of 95% for classification of benign versus malignant tissue and 81% for Gleason grading for data from the same site as the training data. The corresponding accuracies for images from other sites are in average 88% and 52% respectively.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Radiologi och bildbehandling

Nyckelord

Originalspråkengelska
Titel på värdpublikation2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
FörlagIEEE Computer Society
Sidor191-194
Antal sidor4
Volym2018-April
ISBN (elektroniskt)9781538636367
StatusPublished - 2018 maj 23
PublikationskategoriForskning
Peer review utfördJa
Evenemang15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, USA
Varaktighet: 2018 apr 42018 apr 7

Konferens

Konferens15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
LandUSA
OrtWashington
Period2018/04/042018/04/07

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