Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review

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 i...

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Veröffentlicht in:Journal of pathology informatics Jg. 12; H. 1; S. 43
Hauptverfasser: Jose, Laya, Liu, Sidong, Russo, Carlo, Nadort, Annemarie, Di Ieva, Antonio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 01.01.2021
Wolters Kluwer - Medknow
Elsevier
Schlagworte:
Artificial intelligence
deep learning
digital pathology
generative adversarial networks
histopathology
image processing
Review
whole-slide imaging
generative adversarial networks
image processing
deep learning
digital pathology
histopathology
whole-slide imaging
Artificial intelligence
ISSN:2153-3539, 2229-5089, 2153-3539
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:2153-3539
2229-5089
2153-3539
DOI:10.4103/jpi.jpi_103_20