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Generalization of prostate cancer classification for multiple sites using deep learning

Arvidsson, Ida LU ; Overgaard, Niels Christian LU ; Marginean, Felicia Elena LU ; Krzyzanowska, Agnieszka LU ; Bjartell, Anders LU ; Astrom, Kalle LU and Heyden, Anders LU (2018) 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 2018-April. p.191-194
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... (More)

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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Autoencoder, Convolutional neural network, Digital stain separation, Gleason grade, Prostate cancer
host publication
2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
volume
2018-April
pages
4 pages
publisher
IEEE Computer Society
conference name
15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
conference location
Washington, United States
conference dates
2018-04-04 - 2018-04-07
external identifiers
  • scopus:85048138355
ISBN
9781538636367
DOI
10.1109/ISBI.2018.8363552
language
English
LU publication?
yes
id
3794a793-341a-4efe-adc1-e19d9d3130a6
date added to LUP
2018-06-19 14:01:40
date last changed
2019-03-12 04:11:56
@inproceedings{3794a793-341a-4efe-adc1-e19d9d3130a6,
  abstract     = {<p>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&amp;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.</p>},
  author       = {Arvidsson, Ida and Overgaard, Niels Christian and Marginean, Felicia Elena and Krzyzanowska, Agnieszka and Bjartell, Anders and Astrom, Kalle and Heyden, Anders},
  isbn         = {9781538636367},
  keyword      = {Autoencoder,Convolutional neural network,Digital stain separation,Gleason grade,Prostate cancer},
  language     = {eng},
  location     = {Washington, United States},
  month        = {05},
  pages        = {191--194},
  publisher    = {IEEE Computer Society},
  title        = {Generalization of prostate cancer classification for multiple sites using deep learning},
  url          = {http://dx.doi.org/10.1109/ISBI.2018.8363552},
  volume       = {2018-April},
  year         = {2018},
}