ReviewPatterns (New York, N.Y.)2020
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential.
Review in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Generative Artificial Intelligence in Prostate Cancer Imaging.Balkan medical journal · 2025Review
- Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.The journal of pathology. Clinical research · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer.iScience · 2024Article
- Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer.Scientific reports · 2024Article
- Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling.Nature machine intelligence · 2024Article
- A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.Scientific data · 2023Article
- Plantorganelle Hunter is an effective deep-learning-based method for plant organelle phenotyping in electron microscopy.Nature plants · 2023Article
- Deep learning generates synthetic cancer histology for explainability and education.NPJ precision oncology · 2023Article
- Enhanced Pathology Image Quality with Restore-Generative Adversarial Network.The American journal of pathology · 2023Article
- Article
- Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy.Nature biomedical engineering · 2023Article
- Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors - Analytical Validation.International journal of general medicine · 2023Article
- The state of the art for artificial intelligence in lung digital pathology.The Journal of pathology · 2022Review
- Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images.Journal of clinical medicine · 2022Article
- Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results.Patterns (New York, N.Y.) · 2022Article
- Deep Learning-Inferred Multiplex ImmunoFluorescence for Immunohistochemical Image Quantification.Nature machine intelligence · 2022Article
- Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey.Eye and vision (London, England) · 2022Review
- Three-dimensional label-free histological imaging of whole organs by microtomy-assisted autofluorescence tomography.iScience · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole-slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail at the same time. Simultaneously, novel machine-learning algorithms have boosted the performance of image analysis approaches. In this paper, we focus on a particularly powerful class of architectures, the so-called generative adversarial networks (GANs) applied to histological image data. Besides improving performance, GANs also enable previously intractable application scenarios in this field. However, GANs could exhibit a potential for introducing bias. Hereby, we summarize the recent state-of-the-art developments in a generalizing notation, present the main applications of GANs, and give an outlook of some chosen promising approaches and their possible future applications. In addition, we identify currently unavailable methods with potential for future applications.
Indexed as
Identifiers
What OpenQuestion holds
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.