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Comparison of different augmentation techniques for improved generalization performance for gleason grading

Arvidsson, Ida LU ; Overgaard, Niels Christian LU ; Astrom, Kalle LU orcid and Heyden, Anders LU orcid (2019) 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 p.923-927
Abstract

The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we have used cycle generative adversarial networks (GAN); either to generate more examples to train on or to transform images from one site to another. Furthermore, we have investigated to what extent standard augmentation techniques improve the generalization performance. We performed experiments on four datasets with slides from prostate... (More)

The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we have used cycle generative adversarial networks (GAN); either to generate more examples to train on or to transform images from one site to another. Furthermore, we have investigated to what extent standard augmentation techniques improve the generalization performance. We performed experiments on four datasets with slides from prostate biopsies, stained with HE, detailed annotated with Gleason grades. We obtained results similar to previous studies, with accuracies of 77% for Gleason grading for images from the same site as the training data and 59% for images from other sites. However, we also found out that the use of traditional augmentation techniques gave better performance compared to when using cycle GANs, either to augment the training data or to normalize the test data.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Augmentation, Convolutional neural network, Cycle generative adversarial network, Deep learning, Digital pathology, Gleason grading, Prostate cancer
host publication
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
article number
8759264
pages
5 pages
publisher
IEEE Computer Society
conference name
16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
conference location
Venice, Italy
conference dates
2019-04-08 - 2019-04-11
external identifiers
  • scopus:85073914841
ISBN
9781538636411
DOI
10.1109/ISBI.2019.8759264
language
English
LU publication?
yes
id
32f41e4b-d2e7-4f3c-a45a-9e2aed57cb5d
date added to LUP
2019-11-06 11:04:35
date last changed
2023-12-04 03:43:17
@inproceedings{32f41e4b-d2e7-4f3c-a45a-9e2aed57cb5d,
  abstract     = {{<p>The fact that deep learning based algorithms used for digital pathology tend to overfit to the site of the training data is well-known. Since an algorithm that does not generalize is not very useful, we have in this work studied how different data augmentation techniques can reduce this problem but also how data from different sites can be normalized to each other. For both of these approaches we have used cycle generative adversarial networks (GAN); either to generate more examples to train on or to transform images from one site to another. Furthermore, we have investigated to what extent standard augmentation techniques improve the generalization performance. We performed experiments on four datasets with slides from prostate biopsies, stained with HE, detailed annotated with Gleason grades. We obtained results similar to previous studies, with accuracies of 77% for Gleason grading for images from the same site as the training data and 59% for images from other sites. However, we also found out that the use of traditional augmentation techniques gave better performance compared to when using cycle GANs, either to augment the training data or to normalize the test data.</p>}},
  author       = {{Arvidsson, Ida and Overgaard, Niels Christian and Astrom, Kalle and Heyden, Anders}},
  booktitle    = {{2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}},
  isbn         = {{9781538636411}},
  keywords     = {{Augmentation; Convolutional neural network; Cycle generative adversarial network; Deep learning; Digital pathology; Gleason grading; Prostate cancer}},
  language     = {{eng}},
  pages        = {{923--927}},
  publisher    = {{IEEE Computer Society}},
  title        = {{Comparison of different augmentation techniques for improved generalization performance for gleason grading}},
  url          = {{http://dx.doi.org/10.1109/ISBI.2019.8759264}},
  doi          = {{10.1109/ISBI.2019.8759264}},
  year         = {{2019}},
}