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

Ida Arvidsson; Niels Christian Overgaard; Agnieszka Krzyzanowska; Felicia-Elena Marginean; Athanasios Simoulis; Anders Bjartell; Kalle Aström; Anders Heyden

doi:10.1117/12.2549011

16 March 2020 Domain-adversarial neural network for improved generalization performance of Gleason grade classification

Ida Arvidsson, Niels Christian Overgaard, Agnieszka Krzyzanowska, Felicia-Elena Marginean, Athanasios Simoulis, Anders Bjartell, Kalle Aström, Anders Heyden

Author Affiliations +

Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 1132016 (2020) https://doi.org/10.1117/12.2549011
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

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 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.

Citation Download Citation

Ida Arvidsson, Niels Christian Overgaard, Agnieszka Krzyzanowska, Felicia-Elena Marginean, Athanasios Simoulis, Anders Bjartell, Kalle Aström, and Anders Heyden "Domain-adversarial neural network for improved generalization performance of Gleason grade classification", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132016 (16 March 2020); https://doi.org/10.1117/12.2549011

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available