Generative adversarial networks in digital pathology and histopathological image processing: a review

Laya Jose*, Sidong Liu, Carlo Russo, Annemarie Nadort, Antonio Di Ieva

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)
23 Downloads (Pure)

Abstract

Digital pathology is gaining prominence among the researchers with developments in advanced imaging modalities and new technologies. Generative adversarial networks (GANs) are a recent development in the field of artificial intelligence and since their inception, have boosted considerable interest in digital pathology. GANs and their extensions have opened several ways to tackle many challenging histopathological image processing problems such as color normalization, virtual staining, ink removal, image enhancement, automatic feature extraction, segmentation of nuclei, domain adaptation and data augmentation. This paper reviews recent advances in histopathological image processing using GANs with special emphasis on the future perspectives related to the use of such a technique. The papers included in this review were retrieved by conducting a keyword search on Google Scholar and manually selecting the papers on the subject of H&E stained digital pathology images for histopathological image processing. In the first part, we describe recent literature that use GANs in various image preprocessing tasks such as stain normalization, virtual staining, image enhancement, ink removal, and data augmentation. In the second part, we describe literature that use GANs for image analysis, such as nuclei detection, segmentation, and feature extraction. This review illustrates the role of GANs in digital pathology with the objective to trigger new research on the application of generative models in future research in digital pathology informatics.
Original languageEnglish
Article number43
Pages (from-to)1-11
Number of pages11
JournalJournal of Pathology Informatics
Volume12
DOIs
Publication statusPublished - 3 Nov 2021

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Artificial intelligence
  • deep learning
  • digital pathology
  • generative adversarial networks
  • histopathology
  • image processing
  • whole‑slide imaging

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