ArticleThe British journal of radiology2019
Determination of mammographic breast density using a deep convolutional neural network.
Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Deep Learning-Based Automated Reports for Breast Density Assessment in Mammography Images.Journal of imaging informatics in medicine · 2026Article
- An Unsupervised Deep Learning Framework for Quantitative Breast Density Estimation from Mammograms.Journal of imaging · 2026Article
- Breast density in MRI: an AI-based quantification and relationship to assessment in mammography.NPJ breast cancer · 2025Article
- Assessing the generalisation of artificial intelligence across mammography manufacturers.PLOS digital health · 2025Article
- Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review.Journal of imaging · 2025Review
- Artificial intelligence and consistency in patient care: a large-scale longitudinal study of mammographic density assessment.BJR artificial intelligence · 2025Article
- Evaluating the Effect of MarginProbeAnnals of surgical oncology · 2024Article
- Sentinel Lymph Node Biopsy in Breast Cancer Using Different Types of Tracers According to Molecular Subtypes and Breast Density-A Randomized Clinical Study.Diagnostics (Basel, Switzerland) · 2024Article
- Assessment of the Breast Density Prevalence in Swiss Women with a Deep Convolutional Neural Network: A Cross-Sectional Study.Diagnostics (Basel, Switzerland) · 2024Article
- Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network.Journal of imaging · 2024Article
- Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks.European radiology · 2023Article
- Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks.Insights into imaging · 2023Article
- Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis.Bioengineering (Basel, Switzerland) · 2023Article
- Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.Frontiers in radiology · 2023Article
- Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types.Cancers · 2022Article
- Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification.European radiology experimental · 2022Article
- Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Digital breast tomosynthesis-based peritumoral radiomics approaches in the differentiation of benign and malignant breast lesions.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Article
- Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.Breast cancer research : BCR · 2022Review
- Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?Diagnostics (Basel, Switzerland) · 2022Article
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8 authors.
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Abstract
objectiveHigh breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) Atlas.
methodsIn this study, 20,578 mammography single views from 5221 different patients (58.3 ± 11.5 years) were downloaded from the picture archiving and communications system of our institution and automatically sorted according to the ACR density (a-d) provided by the corresponding radiological reports. A dCNN with 11 convolutional layers and 3 fully connected layers was trained and validated on an augmented dataset. The model was finally tested on two different datasets against: i) the radiological reports and ii) the consensus decision of two human readers. None of the test datasets was part of the dataset used for the training and validation of the algorithm.
resultsThe optimal number of epochs was 91 for medio-lateral oblique (MLO) projections and 94 for cranio-caudal projections (CC), respectively. Accuracy for MLO projections obtained on the validation dataset was 90.9% (CC: 90.1%). Tested on the first test dataset of mammographies (850 MLO and 880 CC), the algorithm showed an accordance with the corresponding radiological reports of 71.7% for MLO and of 71.0% for CC. The agreement with the radiological reports improved in the differentiation between dense and fatty breast for both projections (MLO = 88.6% and CC = 89.9%). In the second test dataset of 200 mammographies, a good accordance was found between the consensus decision of the two readers on both, the MLO-model (92.2%) and the right craniocaudal-model (87.4%). In the differentiation between fatty (ACR A/B) and dense breasts (ACR C/D), the agreement reached 99% for the MLO and 96% for the CC projections, respectively.
conclusionsThe dCNN allows for accurate classification of breast density based on the ACR BI-RADS system. The proposed technique may allow accurate, standardized, and observer independent breast density evaluation of mammographies. ADVANCES IN KNOWLEDGE: Standardized classification of mammographies by a dCNN could lead to a reduction of falsely classified breast densities, thereby allowing for a more accurate breast cancer risk assessment for the individual patient and a more reliable decision, whether additional ultrasound is recommended.
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