ArticleScientific reports2023
Unsupervised anomaly detection with generative adversarial networks in mammography.
Article in Scientific reports, 2023. 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 10 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
- Bayesian Conditional GAN for Unsupervised Anomaly Detection in Structural Health Monitoring Time-Series Dataset.Sensors (Basel, Switzerland) · 2026Article
- Dataset of artificial breast cancer MRIs produced from unpaired mammograms.Data in brief · 2026Article
- The Clinical Significance of Imaging Biomarkers Discoverable by Anomaly Detection Methods in Retinal Diseases: A Review.Journal of ophthalmology · 2026Review
- Enhancing Breast Lesion Detection in Mammograms via Transfer Learning.Journal of imaging · 2025Article
- Breast Cancer Detection via Multi-Tiered Self-Contrastive Learning in Microwave Radiometric Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluating normative representation learning in generative AI for robust anomaly detection in brain imaging.Nature communications · 2025Article
- A Review of the Applications, Benefits, and Challenges of Generative AI for Sustainable Toxicology.Current research in toxicology · 2025Review
- Insights and Considerations in Development and Performance Evaluation of Generative Adversarial Networks (GANs): What Radiologists Need to Know.Diagnostics (Basel, Switzerland) · 2024Review
- Cosine similarity knowledge distillation for surface anomaly detection.Scientific reports · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a common cancer among women, and screening mammography is the primary tool for diagnosing this condition. Recent advancements in deep-learning technologies have triggered the implementation of research studies via mammography. Semi-supervised or unsupervised methods are often used to overcome the limitations of supervised learning, such as manpower and time, for labeling in clinical situations where abnormal data are significantly lacking. Accordingly, we proposed a generative model that uses a state-of-the-art generative network (StyleGAN2) to create high-quality synthetic mammographic images and an anomaly detection method to detect breast cancer on mammograms in unsupervised methods. The generation model was trained via only normal mammograms and breast cancer classification was performed via anomaly detection using 50 breast cancer and 50 normal mammograms that did not overlap with the dataset for generative model learning. Our generative model has shown comparable fidelity to real images, and the anomaly detection method via this generative model showed high sensitivity, demonstrating its potential for breast cancer screening. This method could differentiate between normal and cancer-positive mammogram and help overcome the weakness of current supervised methods.
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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.