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

作者：Laya Jose, Sidong Liu, Carlo Russo, Annemarie Nadort, Antonio Di Ieva · 发表于：Journal of Pathology Informatics · 年份：2021 · DOI：10.4103/jpi.jpi_103_20 · 被引用次数：109 · 研究领域：AI in cancer detection、Generative Adversarial Networks and Image Synthesis、Cell Image Analysis Techniques

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 m...