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Domain-adversarial neural network for improved generalization performance of gleason grade classification

Arvidsson, Ida LU ; Overgaard, Niels Christian LU ; Krzyzanowska, Agnieszka LU ; Marginean, Felicia Elena LU orcid ; Simoulis, Athanasios LU orcid ; Bjartell, Anders LU ; Aström, Kalle LU orcid and Heyden, Anders LU orcid (2020) Medical Imaging 2020: Digital Pathology In Progress in Biomedical Optics and Imaging - Proceedings of SPIE 11320.
Abstract

When training a deep learning model, the dataset used is of great importance to make sure that the model learns relevant features of the data and that it will be able to generalize to new data. However, it is typically difficult to produce a dataset without some bias toward any specific feature. Deep learning models used in histopathology have a tendency to overfit to the stain appearance of the training data - if the model is trained on data from one lab only, it will usually not be able to generalize to data from other labs. The standard technique to overcome this problem is to use color augmentation of the training data which, artificially, generates more variations for the network to learn. In this work we instead test the use of a... (More)

When training a deep learning model, the dataset used is of great importance to make sure that the model learns relevant features of the data and that it will be able to generalize to new data. However, it is typically difficult to produce a dataset without some bias toward any specific feature. Deep learning models used in histopathology have a tendency to overfit to the stain appearance of the training data - if the model is trained on data from one lab only, it will usually not be able to generalize to data from other labs. The standard technique to overcome this problem is to use color augmentation of the training data which, artificially, generates more variations for the network to learn. In this work we instead test the use of a so called domain-adversarial neural network, which is designed to prevent the model from being biased towards features that in reality are irrelevant such as the origin of an image. To test the technique, four datasets from different hospitals for Gleason grading of prostate cancer are used. We achieve state of the art results for these particular datasets, and furthermore for two of our three test datasets the approach outperforms the use of color augmentation.

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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
Domain adversarial neural network, Generalization, Gleason grading
host publication
Medical Imaging 2020 : Digital Pathology - Digital Pathology
series title
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
editor
Tomaszewski, John E. and Ward, Aaron D.
volume
11320
article number
1132016
publisher
SPIE
conference name
Medical Imaging 2020: Digital Pathology
conference location
Houston, United States
conference dates
2020-02-19 - 2020-02-20
external identifiers
  • scopus:85120979234
ISSN
1605-7422
ISBN
9781510634077
DOI
10.1117/12.2549011
language
English
LU publication?
yes
id
845b99ac-cb9d-4784-8d0a-d3ede62e4ef6
date added to LUP
2022-02-03 14:11:23
date last changed
2023-09-15 18:49:55
@inproceedings{845b99ac-cb9d-4784-8d0a-d3ede62e4ef6,
  abstract     = {{<p>When training a deep learning model, the dataset used is of great importance to make sure that the model learns relevant features of the data and that it will be able to generalize to new data. However, it is typically difficult to produce a dataset without some bias toward any specific feature. Deep learning models used in histopathology have a tendency to overfit to the stain appearance of the training data - if the model is trained on data from one lab only, it will usually not be able to generalize to data from other labs. The standard technique to overcome this problem is to use color augmentation of the training data which, artificially, generates more variations for the network to learn. In this work we instead test the use of a so called domain-adversarial neural network, which is designed to prevent the model from being biased towards features that in reality are irrelevant such as the origin of an image. To test the technique, four datasets from different hospitals for Gleason grading of prostate cancer are used. We achieve state of the art results for these particular datasets, and furthermore for two of our three test datasets the approach outperforms the use of color augmentation.</p>}},
  author       = {{Arvidsson, Ida and Overgaard, Niels Christian and Krzyzanowska, Agnieszka and Marginean, Felicia Elena and Simoulis, Athanasios and Bjartell, Anders and Aström, Kalle and Heyden, Anders}},
  booktitle    = {{Medical Imaging 2020 : Digital Pathology}},
  editor       = {{Tomaszewski, John E. and Ward, Aaron D.}},
  isbn         = {{9781510634077}},
  issn         = {{1605-7422}},
  keywords     = {{Domain adversarial neural network; Generalization; Gleason grading}},
  language     = {{eng}},
  publisher    = {{SPIE}},
  series       = {{Progress in Biomedical Optics and Imaging - Proceedings of SPIE}},
  title        = {{Domain-adversarial neural network for improved generalization performance of gleason grade classification}},
  url          = {{http://dx.doi.org/10.1117/12.2549011}},
  doi          = {{10.1117/12.2549011}},
  volume       = {{11320}},
  year         = {{2020}},
}