Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/13548
Title: Towards histopathological stain invariance by Unsupervised Domain Augmentation using generative adversarial networks
Authors: Vasiljević, Jelica
Feuerhake F.
Wemmert, Cedric
Lampert, Thomas
Journal: Neurocomputing
Issue Date: 14-Oct-2021
Abstract: The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift. Digital Pathology is an area prone to high variability due to many sources, including the common practice of evaluating several consecutive tissue sections stained with different staining protocols. Obtaining labels for each stain is very expensive and time consuming as it requires a high level of domain knowledge. In this article, we propose an unsupervised augmentation approach based on adversarial image-to-image translation, which facilitates the training of stain invariant supervised convolutional neural networks. By training the network on one commonly used staining modality and applying it to images that include corresponding, but differently stained, tissue structures, the presented method demonstrates significant improvements over other approaches. These benefits are illustrated in the problem of glomeruli segmentation in seven different staining modalities (PAS, Jones H&E, CD68, Sirius Red, CD34, H&E and CD3) and analysis of the learned representations demonstrate their stain invariance.
URI: https://scidar.kg.ac.rs/handle/123456789/13548
Type: article
DOI: 10.1016/j.neucom.2021.07.005
ISSN: 09252312
SCOPUS: 85111008525
Appears in Collections:Faculty of Science, Kragujevac

Page views(s)

43

Downloads(s)

5

Files in This Item:
File Description SizeFormat 
10.1016-j.neucom.2021.07.005.pdf21.57 MBAdobe PDFThumbnail
View/Open


Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.