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 language | English |
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Title of host publication | Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23) |
Editors | Jiman Hong, Maart Lanperne, Juw Won Park, Tomas Cerny, Hossain Shahriar |
Place of Publication | New York, NY |
Publisher | ACM |
Pages | 1197-1204 |
Number of pages | 8 |
ISBN (Electronic) | 9781450395175 |
DOIs | |
Publication status | Published - 27 Mar 2023 |
Event | 38th Annual ACM Symposium on Applied Computing, SAC 2023 - Tallinn, Estonia Duration: 27 Mar 2023 → 31 Mar 2023 |
Conference
Conference | 38th Annual ACM Symposium on Applied Computing, SAC 2023 |
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Country/Territory | Estonia |
City | Tallinn |
Period | 27/03/23 → 31/03/23 |
Keywords
- Digital pathology
- Generative adversarial networks
- Multi-domain image translation
- Semantic structure