ArticleJapanese journal of radiology2019
Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.
Article in Japanese journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.
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Who cites it
82 citing papers in PubMed, 1 synthesis or guideline pooled it, 177 citations in OpenAlex.
- Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis.Surgical endoscopy · 2023Pooled it
- Comparison of breast ultrasound image classification accuracy between convolutional neural networks and human experts using multicenter external validation cohort data.Journal of medical ultrasonics (2001) · 2026Article
- Transformers meet CNNs for insights into breast mass classification from histopathological images.Frontiers in artificial intelligence · 2026Article
- Research on the establishment of a four-classification model for breast mass ultrasound images based on transfer learning.PloS one · 2026Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Depth-Dependent Variability in Ultrasound Attenuation Imaging for Hepatic Steatosis: A Pilot Study of ATI and HRI in Healthy Volunteers.Journal of imaging · 2025Article
- Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation.Current oncology (Toronto, Ont.) · 2025Article
- Breast Cancer Detection on Dual-View Sonography via Data-Centric Deep Learning.IEEE open journal of engineering in medicine and biology · 2025Article
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- Deep learning-based osteochondritis dissecans detection in ultrasound images with humeral capitellum localization.International journal of computer assisted radiology and surgery · 2024Article
- Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?European radiology experimental · 2024Article
- Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations.European radiology · 2024Article
- Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2024Article
- Role of Radiology in the Diagnosis and Treatment of Breast Cancer in Women: A Comprehensive Review.Cureus · 2024Review
- Research on ultrasound-based radiomics: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Artificial Intelligence-Based Diagnosis of Gastric Mesenchymal Tumors Using Digital Endosonography Image Analysis.Journal of clinical medicine · 2024Article
- Artificial intelligence-assisted ultrasound imaging in hemophilia: research, development, and evaluation of hemarthrosis and synovitis detection.Research and practice in thrombosis and haemostasis · 2024Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
22 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
purposeWe aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound. MATERIALS AND
methodsWe retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. Deep learning model was constructed using CNN architecture GoogLeNet and analyzed test data: 48 benign masses, 72 malignant masses. Three radiologists interpreted these test data. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.
resultsThe CNN model and radiologists had a sensitivity of 0.958 and 0.583-0.917, specificity of 0.925 and 0.604-0.771, and accuracy of 0.925 and 0.658-0.792, respectively. The CNN model had equal or better diagnostic performance compared to radiologists (AUC = 0.913 and 0.728-0.845, p = 0.01-0.14).
conclusionDeep learning with CNN shows high diagnostic performance to discriminate between benign and malignant breast masses on ultrasound.
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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.