ReviewJournal of pathology informatics2021
Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review.
Review in Journal of pathology informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.PloS one · 2025Pooled it
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- Insights Into Spatial Transcriptomics: Exploring Recent Technical Developments and Their Diverse Applications.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- Bridging traditional and deep learning methods in H&E histological image normalization: a comprehensive review and introducing a novel framework for comparative analyses.Journal of advanced research · 2026Review
- Pathology-informed Generative Adversarial Network Augmentation Improves Classification of Peripheral Nerve Sheath Tumors by Modeling Morphological Variability: A Pilot Investigation.Head and neck pathology · 2026Article
- Evaluating StyleGAN2-ADA and f-AnoGAN for GAN based unsupervised anomaly detection in gastrointestinal endoscopy.Scientific reports · 2026Article
- Impact of tissue staining and scanner variation on the performance of pathology foundation models: a study of sarcomas and their mimics.The journal of pathology. Clinical research · 2026Article
- The state of the art in artificial intelligence and digital pathology in prostate cancer.Nature reviews. Urology · 2026Review
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology.Cancer research · 2025Article
- Enhancing Melanoma Diagnosis in Histopathology with Deep Learning and Synthetic Data Augmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.Breast cancer research : BCR · 2025Article
- Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models.NPJ digital medicine · 2025Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- Deeply supervised two stage generative adversarial network for stain normalization.Scientific reports · 2025Article
- Out-of-distribution generalization for segmentation of lymph node metastasis in breast cancer.Scientific reports · 2025Article
- Application of AI in the identification of gastrointestinal stromal tumors: a comprehensive analysis based on pathological, radiological, and genetic variation features.Frontiers in genetics · 2025Review
- Tempered enthusiasm by interviewed experts for synthetic data and ELSI checklists for AI in medicine.AI and ethics · 2025Article
- HcGAN: Harmonic conditional generative adversarial network for efficiently generating high-quality IHC images from H&E.Heliyon · 2024Article
- Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning.Heliyon · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.