ArticlePloS one2018
Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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
17 citing papers in PubMed, 48 citations in OpenAlex.
- Retinal OCT image classification based on MGR-GAN.Medical & biological engineering & computing · 2025Article
- Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics.Frontiers in oncology · 2025Article
- Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.IEEE open journal of engineering in medicine and biology · 2025Article
- Metaplastic Breast Cancer: Current Understanding and Future Directions.Clinical breast cancer · 2023Review
- Rare subtypes of triple negative breast cancer: Current understanding and future directions.NPJ breast cancer · 2023Review
- Deepfake Histologic Images for Enhancing Digital Pathology.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- AI in Breast Cancer Imaging: A Survey of Different Applications.Journal of imaging · 2022Review
- Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures.BioMed research international · 2022Review
- IHC-based Ki67 as response biomarker to tamoxifen in breast cancer window trials enrolling premenopausal women.NPJ breast cancer · 2021Article
- 3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images.Entropy (Basel, Switzerland) · 2021Article
- Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential.Patterns (New York, N.Y.) · 2020Review
- From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care.Yearbook of medical informatics · 2020Review
- Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma.Diagnostic pathology · 2020Article
- Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches.Frontiers in neuroscience · 2020Article
- A modular cGAN classification framework: Application to colorectal tumor detection.Scientific reports · 2019Article
- Deep Learning in Medical Imaging.Neurospine · 2019Article
- Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images.Frontiers in medicine · 2019Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
In pathology, Immunohistochemical staining (IHC) of tissue sections is regularly used to diagnose and grade malignant tumors. Typically, IHC stain interpretation is rendered by a trained pathologist using a manual method, which consists of counting each positively- and negatively-stained cell under a microscope. The manual enumeration suffers from poor reproducibility even in the hands of expert pathologists. To facilitate this process, we propose a novel method to create artificial datasets with the known ground truth which allows us to analyze the recall, precision, accuracy, and intra- and inter-observer variability in a systematic manner, enabling us to compare different computer analysis approaches. Our method employs a conditional Generative Adversarial Network that uses a database of Ki67 stained tissues of breast cancer patients to generate synthetic digital slides. Our experiments show that synthetic images are indistinguishable from real images. Six readers (three pathologists and three image analysts) tried to differentiate 15 real from 15 synthetic images and the probability that the average reader would be able to correctly classify an image as synthetic or real more than 50% of the time was only 44.7%.
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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.