ArticleDiagnostics (Basel, Switzerland)2020
Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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
15 citing papers in PubMed, 38 citations in OpenAlex.
- The Clinical Significance of Imaging Biomarkers Discoverable by Anomaly Detection Methods in Retinal Diseases: A Review.Journal of ophthalmology · 2026Review
- Artificial intelligence-enhanced handheld breast ultrasound for screening: A systematic review of diagnostic test accuracy.PLOS digital health · 2025Article
- Generative Adversarial Networks (GANs) in the Field of Head and Neck Surgery: Current Evidence and Prospects for the Future-A Systematic Review.Journal of clinical medicine · 2024Review
- Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning.Korean journal of radiology · 2023Review
- Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.European radiology experimental · 2023Article
- Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network.Diagnostics (Basel, Switzerland) · 2022Article
- Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging.Scientific reports · 2022Article
- Emergency triage of brain computed tomography via anomaly detection with a deep generative model.Nature communications · 2022Article
- Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder.Journal of digital imaging · 2022Article
- Contrastive learning-guided multi-meta attention network for breast ultrasound video diagnosis.Frontiers in oncology · 2022Article
- Investigating the Image Quality and Utility of Synthetic MRI in the Breast.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2021Article
- Clinical Usefulness of Ultrasound-Guided Fine Needle Aspiration and Core Needle Biopsy for Patients with Axillary Lymphadenopathy.Medicina (Kaunas, Lithuania) · 2021Article
- Machine Learning Models to Improve the Differentiation Between Benign and Malignant Breast Lesions on Ultrasound: A Multicenter External Validation Study.Cancer management and research · 2021Article
- The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.Diagnostics (Basel, Switzerland) · 2020Review
- Evaluation of the Usefulness of CO-RADS for Chest CT in Patients Suspected of Having COVID-19.Diagnostics (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
Abstract
We aimed to use generative adversarial network (GAN)-based anomaly detection to diagnose images of normal tissue, benign masses, or malignant masses on breast ultrasound. We retrospectively collected 531 normal breast ultrasound images from 69 patients. Data augmentation was performed and 6372 (531 × 12) images were available for training. Efficient GAN-based anomaly detection was used to construct a computational model to detect anomalous lesions in images and calculate abnormalities as an anomaly score. Images of 51 normal tissues, 48 benign masses, and 72 malignant masses were analyzed for the test data. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of this anomaly detection model were calculated. Malignant masses had significantly higher anomaly scores than benign masses (
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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.