MultiPathGAN: structure preserving stain normalization using unsupervised multi-domain adversarial network with perception loss

Haseeb Nazki*, Ognjen Arandjelović, In Hwa Um, David Harrison

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Histopathology relies on the analysis of microscopic tissue images to diagnose disease. A crucial part of tissue preparation is staining whereby a dye is used to make the salient tissue components more distinguishable. However, differences in laboratory protocols and scanning devices result in significant confounding appearance variation in the corresponding images. This variation increases both human error and the inter-rater variability, as well as hinders the performance of automatic or semi-automatic methods. In the present paper we introduce an unsupervised adversarial network to translate (and hence normalize) whole slide images across multiple data acquisition domains. Our key contributions are: (i) an adversarial architecture which learns across multiple domains with a single generator-discriminator network using an information flow branch which optimizes for perceptual loss, and (ii) the inclusion of an additional feature extraction network during training which guides the transformation network to keep all the structural features in the tissue image intact. We: (i) demonstrate the effectiveness of the proposed method firstly on H&E slides of 120 cases of kidney cancer, as well as (ii) show the benefits of the approach on more general problems, such as flexible illumination based natural image enhancement and light source adaptation.

Original languageEnglish
Title of host publicationProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23)
EditorsJiman Hong, Maart Lanperne, Juw Won Park, Tomas Cerny, Hossain Shahriar
Place of PublicationNew York, NY
PublisherACM
Pages1197-1204
Number of pages8
ISBN (Electronic)9781450395175
DOIs
Publication statusPublished - 27 Mar 2023
Event38th Annual ACM Symposium on Applied Computing, SAC 2023 - Tallinn, Estonia
Duration: 27 Mar 202331 Mar 2023

Conference

Conference38th Annual ACM Symposium on Applied Computing, SAC 2023
Country/TerritoryEstonia
CityTallinn
Period27/03/2331/03/23

Keywords

  • Digital pathology
  • Generative adversarial networks
  • Multi-domain image translation
  • Semantic structure

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