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Variability in Interpretive Performance at Screening Mammography and Radiologists’ Characteristics Associated with Accuracy
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/archinte.156.2.209openalex-snapshot
Variability in the interpretation of screening mammograms by US radiologists. Findings from a national sample
inferredcites →https://doi.org/10.1001/archinte.1996.00440020119016openalex-snapshot
Variability in the Interpretation of Screening Mammograms by US Radiologists
inferredcites →https://doi.org/10.1001/jama.290.16.2129openalex-snapshot
Comparison of Screening Mammography in the United States and the United Kingdom
inferredcites →https://doi.org/10.1001/jama.291.4.442openalex-snapshot
Effect of Breast Augmentation on the Accuracy of Mammography and Cancer Characteristics
inferredcites →https://doi.org/10.1002/sim.942openalex-snapshot
A hierarchical regression approach to meta‐analysis of diagnostic test accuracy evaluations
inferredcites →https://doi.org/10.1016/s0033-8389(01)00017-3openalex-snapshot
Breast imaging reporting and data system (BI-RADS)
inferredcites →https://doi.org/10.1016/s0895-4356(97)00126-1openalex-snapshot
Response rates to mail surveys published in medical journals
inferredcites →https://doi.org/10.1056/nejm199412013312206openalex-snapshot
Variability in Radiologists' Interpretations of Mammograms
inferredcites →https://doi.org/10.1093/jnci/92.20.1657openalex-snapshot
Predicting the Cumulative Risk of False-Positive Mammograms
inferredcites →https://doi.org/10.1093/jnci/94.18.1373openalex-snapshot
Screening Mammograms by Community Radiologists: Variability in False-Positive Rates
inferredcites →https://doi.org/10.1093/jnci/94.20.1546openalex-snapshot
Detection of Ductal Carcinoma In Situ in Women Undergoing Screening Mammography
inferredcites →https://doi.org/10.1093/jnci/djh333openalex-snapshot
Accuracy of Screening Mammography Interpretation by Characteristics of Radiologists
inferredcites →https://doi.org/10.1093/jnci/dji060openalex-snapshot
Physician Predictors of Mammographic Accuracy
inferredcites →https://doi.org/10.1093/jnci/djm238openalex-snapshot
Radiologist Characteristics Associated With Interpretive Performance of Diagnostic Mammography
inferredcites →https://doi.org/10.1148/radiol.2222010647openalex-snapshot
Concordance of Breast Imaging Reporting and Data System Assessments and Management Recommendations in Screening Mammography
inferredcites →https://doi.org/10.1148/radiol.2243011482openalex-snapshot
Performance Parameters for Screening and Diagnostic Mammography: Specialist and General Radiologists
inferredcites →https://doi.org/10.1148/radiol.2272021163openalex-snapshot
Generalists versus Specialists in Mammography [letter]
inferredcites →https://doi.org/10.1148/radiol.2273020046openalex-snapshot
Survey of Radiology Residents: Breast Imaging Training and Attitudes
inferredcites →https://doi.org/10.1148/radiol.2342040048openalex-snapshot
Association between Mammography Timing and Measures of Screening Performance in the United States
inferredcites →https://doi.org/10.1148/radiol.2361040512openalex-snapshot
Does Litigation Influence Medical Practice? The Influence of Community Radiologists' Medical Malpractice Perceptions and Experience on Screening Mammography
inferredcites →https://doi.org/10.1148/radiol.2411051504openalex-snapshot
Performance Benchmarks for Screening Mammography
inferredcites →https://doi.org/10.1148/radiology.135.2.7367658openalex-snapshot
Mammography training in diagnostic radiology residency programs.
inferredcites →https://doi.org/10.1148/radiology.184.1.1609100openalex-snapshot
Improvement in mammography interpretation skills in a community radiology practice after dedicated teaching courses: 2-year medical audit of 38,633 cases.
inferredcites →https://doi.org/10.1148/radiology.201.3.8939230openalex-snapshot
Status of mammography education and knowledge of radiology residents.
inferredcites →https://doi.org/10.1200/jco.2003.05.151openalex-snapshot
Prognostic Characteristics of Breast Cancer Among Postmenopausal Hormone Users in a Screened Population
inferredcites →https://doi.org/10.2214/ajr.05.1581openalex-snapshot
Performance Parameters for Screening and Diagnostic Mammography in a Community Practice: Are There Differences Between Specialists and General Radiologists?
inferredcites →https://doi.org/10.2214/ajr.156.1.1898571openalex-snapshot
Present status of residency training in mammography.
inferredcites →https://doi.org/10.2214/ajr.160.2.8424332openalex-snapshot
Quality assurance in mammography: status of residency education.
inferredcites →https://doi.org/10.2214/ajr.166.5.8615267openalex-snapshot
Status of residency training in mammography.
inferredcites →https://doi.org/10.2214/ajr.169.4.9308451openalex-snapshot
Breast Cancer Surveillance Consortium: a national mammography screening and outcomes database.
inferredcites →https://doi.org/10.3928/00220124-20080301-09openalex-snapshot
Continuing Education in the Health Professions: Improving Healthcare Through Lifelong Learning
inferredcites →https://doi.org/10.7326/0003-4819-133-11-200012050-00009openalex-snapshot
Performance of Screening Mammography among Women with and without a First-Degree Relative with Breast Cancer
inferredcites →https://doi.org/10.7326/0003-4819-137-5_part_1-200209030-00011openalex-snapshot
Screening for Breast Cancer: Recommendations and Rationale
inferredcites →https://doi.org/10.7326/0003-4819-137-5_part_1-200209030-00012openalex-snapshot
Breast Cancer Screening: A Summary of the Evidence for the U.S. Preventive Services Task Force
inferredcites →https://doi.org/10.7326/0003-4819-138-3-200302040-00008openalex-snapshot
Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Histo-Fusion: A Novel Domain Specific Learning to Identify Invasive Ductal Carcinoma (IDC) from Histopathological Images
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Quantifying the Benefits and Harms of Screening Mammography
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External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
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Risk of breast cancer after false‐positive results in mammographic screening
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The influence of breast density and key demographics of radiographers on mammography reporting performance – a pilot study
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Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms
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Combining Deep Learning and Structured Prediction for Segmenting Masses in Mammograms
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The Automated Learning of Deep Features for Breast Mass Classification from Mammograms
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A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification
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Should CAD Be Used as a Second Reader? – Exploring Two Alternative Reading Modes for CAD in Screening Mammography
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A Study on Deep Learning for Breast Cancer Detection in Histopathological Images
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Breast Cancer Detection Based on Decision Fusion of Machine Learning Algorithms
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Breast cancer screening in England and the United States: a comparison of provision and utilisation
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Künstliche Intelligenz in der modernen Mammadiagnostik
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Experiences with a self-test for Dutch breast screening radiologists: lessons learnt
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Variations in screening outcome among pairs of screening radiologists at non-blinded double reading of screening mammograms: a population-based study
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Nonpalpable breast lesions: impact of a second-opinion review at a breast unit on BI-RADS classification
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Optimum screening mammography reading volumes: evidence from the NHS Breast Screening Programme
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Long-term prognostic implications of AI-detected versus AI-undetected breast cancers on mammography: a propensity score-matched analysis
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Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
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Beyond the black box: lessons in explainability from AI in mammography
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The Athena Breast Health Network: developing a rapid learning system in breast cancer prevention, screening, treatment, and care
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A machine learning model based on readers’ characteristics to predict their performances in reading screening mammograms
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Development of a deep learning-based automated diagnostic system (DLADS) for classifying mammographic lesions — a first large-scale multi-institutional clinical trial in Japan
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Optimising Radiologist Performance in Cancer Detection on Mammograms: The Impact of Weekly Reading Hours and Test Set Experience on BreastScreen and Non-BreastScreen Readers
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State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)
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Feasibility and Satisfaction with a Tailored Web-based Audit Intervention for Recalibrating Radiologists’ Thresholds for Conducting Additional Work-up
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Using a Tailored Web-based Intervention to Set Goals to Reduce Unnecessary Recall
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Computer Disease Simulation Models
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An Observer Study for a Computer-Aided Reading Protocol (CARP) in the Screening Environment for Digital Mammography
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Feasibility and Acceptability of Conducting a Randomized Clinical Trial Designed to Improve Interpretation of Screening Mammography
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Variability in Interpretive Performance at Screening Mammography and Radiologists' Characteristics Associated with Accuracy
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Understanding the Mammography Audit
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Breast Screen Reader Assessment Strategy (BREAST): A Research Infrastructure with a Translational Objective
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Efficacy of digital screening mammography
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International evaluation of an AI system for breast cancer screening
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Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback
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Reducing Costs of Breast Examination: Ultrasound Performance and Inter-Observer Variability of Expert Radiologists Versus Residents
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Classification of Breast Cancer using Histology images: Handcrafted and Pre-Trained Features Based Approach
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Artificial intelligence in mammography screening: a narrative review of progress, pitfalls, and potential
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Transitioning to Practice: Getting up to Speed in Efficiency and Accuracy
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Radiologist Interpretive Volume and Breast Cancer Screening Accuracy in a Canadian Organized Screening Program
inferred← citeshttps://doi.org/10.1093/med/9780190063559.003.0004openalex-snapshot
Moral Expertise and Epistemic Peerhood
inferred← citeshttps://doi.org/10.1097/bn9.0000000000000064openalex-snapshot
The Accuracy of Surgeons’ Intuitive Diagnosis of Osteoporosis Using Radiographs and Computed Tomography Images of the Lumbar Spine
inferred← citeshttps://doi.org/10.1097/md.0000000000020977openalex-snapshot
A deep learning-based automated diagnostic system for classifying mammographic lesions
inferred← citeshttps://doi.org/10.1097/mlr.0000000000000393openalex-snapshot
Racial Differences in False-positive Mammogram Rates
inferred← citeshttps://doi.org/10.1097/mlr.0b013e3182407c8aopenalex-snapshot
Facility Characteristics do not Explain Higher False-positive Rates in Diagnostic Mammography at Facilities Serving Vulnerable Women
inferred← citeshttps://doi.org/10.1097/mlr.0b013e3182a303d7openalex-snapshot
Validation of a Medicare Claims-based Algorithm for Identifying Breast Cancers Detected at Screening Mammography
inferred← citeshttps://doi.org/10.1101/752246openalex-snapshot
Perceptual learning of lesions in mammograms induced by response feedback during training
inferred← citeshttps://doi.org/10.1109/bibm.2015.7359868openalex-snapshot
Probabilistic visual search for masses within mammography images using deep learning
inferred← citeshttps://doi.org/10.1109/cbms69103.2026.00240openalex-snapshot
Interpretable Hybrid Modeling for Breast Cancer Risk Stratification from Structured Radiology Reports
inferred← citeshttps://doi.org/10.1109/cisp-bmei53629.2021.9624456openalex-snapshot
Breast tumor diagnosis using radiofrequency signals based ultrasound multifeature maps combined with radiomics analysis
inferred← citeshttps://doi.org/10.1109/dicta.2015.7371234openalex-snapshot
Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests
inferred← citeshttps://doi.org/10.1109/embc.2017.8036918openalex-snapshot
Ensemble of convolutional neural networks for classification of breast microcalcification from mammograms
inferred← citeshttps://doi.org/10.1109/iciem54221.2022.9853203openalex-snapshot
Efficient Tumor Classification using GoogleNet Approach to Increase Accuracy in Comparison with ResNet
inferred← citeshttps://doi.org/10.1109/iciot48696.2020.9089535openalex-snapshot
Breast Mass Tumor Classification using Deep Learning
inferred← citeshttps://doi.org/10.1109/icip.2015.7351343openalex-snapshot
Deep structured learning for mass segmentation from mammograms
inferred← citeshttps://doi.org/10.1109/icrc.2017.8123677openalex-snapshot
Computing Based on Material Training: Application to Binary Classification Problems
inferred← citeshttps://doi.org/10.1109/ipta66025.2025.11222017openalex-snapshot
Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification
inferred← citeshttps://doi.org/10.1109/iranianmvip.2017.8342378openalex-snapshot
Breast cancer classification using covariance description in Riemannian geometry
inferred← citeshttps://doi.org/10.1109/iri51335.2021.00035openalex-snapshot
Toward Automatic Mammography Auditing via Universal Language Model Fine Tuning
inferred← citeshttps://doi.org/10.1109/isbi.2015.7163983openalex-snapshot
Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms
inferred← citeshttps://doi.org/10.1109/medai62885.2024.00015openalex-snapshot
Review on Artificial Intelligence in Medicine and Health
inferred← citeshttps://doi.org/10.1109/tcds.2019.2963682openalex-snapshot
An End-to-End Mammogram Diagnosis: A New Multi-Instance and Multiscale Method Based on Single-Image Feature
inferred← citeshttps://doi.org/10.1109/tcds.2021.3135947openalex-snapshot
Deep Multiinstance Mammogram Classification With Region Label Assignment Strategy and Metric-Based Optimization
inferred← citeshttps://doi.org/10.1111/ajco.13429openalex-snapshot
Interpretative characteristics and case features associated with the performances of radiologists in reading mammograms: A study from a non‐screening population in Asia
inferred← citeshttps://doi.org/10.1111/ajco.70203openalex-snapshot
Investigating Artificial Intelligence Performance on Mammographic Cases in High‐ and Low‐Resourced Countries
inferred← citeshttps://doi.org/10.1111/tbj.12443openalex-snapshot
Misclassification of Breast Imaging Reporting and Data System (BI-RADS) Mammographic Density and Implications for Breast Density Reporting Legislation
inferred← citeshttps://doi.org/10.1111/tbj.12845openalex-snapshot
Do they practice what we preach: Findings from over a decade of breast imaging CME
inferred← citeshttps://doi.org/10.1111/tbj.13838openalex-snapshot
BI‐RADS 3 (short‐interval follow‐up) assessment rate at diagnostic mammography: Correlation with recall rates and utilization as a performance benchmark
inferred← citeshttps://doi.org/10.1117/1.jmi.1.1.015501openalex-snapshot
Influence of study design in receiver operating characteristics studies: sequential versus independent reading
inferred← citeshttps://doi.org/10.1117/1.jmi.10.2.025502openalex-snapshot
Variations of image interpretations of radiologists from different populations in mammography and tomosynthesis with different levels of breast density
inferred← citeshttps://doi.org/10.1117/1.jmi.4.2.025502openalex-snapshot
Lack of agreement between radiologists: implications for image-based model observers
inferred← citeshttps://doi.org/10.1117/1.jmi.7.2.022405openalex-snapshot
Is there a safety-net effect with computer-aided detection?
inferred← citeshttps://doi.org/10.1117/12.2042968openalex-snapshot
A comparison of Australian and USA radiologists' performance in detection of breast cancer
inferred← citeshttps://doi.org/10.1117/12.2081749openalex-snapshot
Experience in reading digital images may decrease observer accuracy in mammography
inferred← citeshttps://doi.org/10.1117/12.2217475openalex-snapshot
Investigating the link between the radiological experience and the allocation of an 'equivocal finding'
inferred← citeshttps://doi.org/10.1117/12.2248911openalex-snapshot
The development and testing of a unique and flexible training module for residents and fellows using digital breast tomosythesis (DBT)
inferred← citeshttps://doi.org/10.1117/12.2249341openalex-snapshot
The effect of prevalence of disease on performance of residents and fellows during training for interpreting DBT in a test-train-test observer study
inferred← citeshttps://doi.org/10.1117/12.2293890openalex-snapshot
Expert identification of visual primitives used by CNNs during mammogram classification
inferred← citeshttps://doi.org/10.1117/12.2512720openalex-snapshot
Is there a safety-net effect with computer-aided detection (CAD)?
inferred← citeshttps://doi.org/10.1117/12.3009417openalex-snapshot
Gated axial transformer network for mass segmentation in mammographic
inferred← citeshttps://doi.org/10.1117/12.3009593openalex-snapshot
Mix Transformer depth-wise separable convolution UNet for breast mass segmentation in mammographic
inferred← citeshttps://doi.org/10.1117/12.910596openalex-snapshot
Assembly and evaluation of a training module and dataset with feedback for improved interpretation of digital breast tomosynthesis examinations
inferred← citeshttps://doi.org/10.1118/1.4860955openalex-snapshot
Computer‐aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume
inferred← citeshttps://doi.org/10.1118/1.4885958openalex-snapshot
Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topological soft gradient prescreening method and luminal analysis
inferred← citeshttps://doi.org/10.1140/epjp/s13360-026-07852-wopenalex-snapshot
A hybrid deep learning architecture for breast cancer classification
inferred← citeshttps://doi.org/10.1148/radiol.10101698openalex-snapshot
Influence of Annual Interpretive Volume on Screening Mammography Performance in the United States
inferred← citeshttps://doi.org/10.1148/radiol.11111026openalex-snapshot
Mammographic Interpretive Volume and Diagnostic Mammogram Interpretation Performance in Community Practice
inferred← citeshttps://doi.org/10.1148/radiol.13121487openalex-snapshot
Screening Mammography Recall Rate: Does Practice Site Matter?
inferred← citeshttps://doi.org/10.1148/radiol.13121530openalex-snapshot
National Lung Screening Trial: Variability in Nodule Detection Rates in Chest CT Studies
inferred← citeshttps://doi.org/10.1148/radiol.13122581openalex-snapshot
Markers of Good Performance in Mammography Depend on Number of Annual Readings
inferred← citeshttps://doi.org/10.1148/radiol.14131502openalex-snapshot
Mammographic Performance in a Population-based Screening Program: Before, during, and after the Transition from Screen-Film to Full-Field Digital Mammography
inferred← citeshttps://doi.org/10.1148/radiol.14132806openalex-snapshot
Effect of Radiologists’ Diagnostic Work-up Volume on Interpretive Performance
inferred← citeshttps://doi.org/10.1148/radiol.2015141303openalex-snapshot
Digital Breast Tomosynthesis: State of the Art
inferred← citeshttps://doi.org/10.1148/radiol.2017151936openalex-snapshot
Does Reader Performance with Digital Breast Tomosynthesis Vary according to Experience with Two-dimensional Mammography?
inferred← citeshttps://doi.org/10.1148/radiol.2017170517openalex-snapshot
Screening Digital Mammography Recall Rate: Does It Change with Digital Breast Tomosynthesis Experience?
inferred← citeshttps://doi.org/10.1148/radiol.2017170638openalex-snapshot
Mammography Performance Benchmarks in an Era of Value-based Care
inferred← citeshttps://doi.org/10.1148/radiol.2017170770openalex-snapshot
ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database
inferred← citeshttps://doi.org/10.1148/radiol.2019182475openalex-snapshot
Surveillance Breast MRI and Mammography: Comparison in Women with a Personal History of Breast Cancer
inferred← citeshttps://doi.org/10.1148/radiol.2019182684openalex-snapshot
Influence of Mammography Volume on Radiologists’ Performance: Results from BreastScreen Norway
inferred← citeshttps://doi.org/10.1148/radiol.2019190924openalex-snapshot
Optimizing Breast Cancer Screening Programs: Experience and Structures
inferred← citeshttps://doi.org/10.1148/radiol.2020192212openalex-snapshot
Range of Radiologist Performance in a Population-based Screening Cohort of 1 Million Digital Mammography Examinations
inferred← citeshttps://doi.org/10.1148/radiol.2021203742openalex-snapshot
Narrowing the Gap: Imaging Disparities in Radiology
inferred← citeshttps://doi.org/10.1148/radiol.2021204379openalex-snapshot
Radiologist Characteristics Associated with Interpretive Performance of Screening Mammography: A National Mammography Database (NMD) Study
inferred← citeshttps://doi.org/10.1148/radiol.252005openalex-snapshot
Closing the Performance Gap between Generalists and Breast Imaging Specialists Using a Nationally Deployed AI Workflow for Screening Mammography
inferred← citeshttps://doi.org/10.1148/radiol.2533090070openalex-snapshot
When Radiologists Perform Best: The Learning Curve in Screening Mammogram Interpretation
inferred← citeshttps://doi.org/10.1148/radiol.2533091517openalex-snapshot
Mammography: Yet Another Challenge
inferred← citeshttps://doi.org/10.1148/ryai.230137openalex-snapshot
Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists
inferred← citeshttps://doi.org/10.1148/rycan.230035openalex-snapshot
Improving Radiology Oncologic Imaging Trainee Case Diversity through Automatic Examination Assignment: Retrospective Study from a Tertiary Cancer Center
inferred← citeshttps://doi.org/10.1158/1055-9965.epi-15-0305openalex-snapshot
Racial and Ethnic Disparity in Symptomatic Breast Cancer Awareness despite a Recent Screen: The Role of Tumor Biology and Mammography Facility Characteristics
inferred← citeshttps://doi.org/10.1162/99608f92.8b81b005openalex-snapshot
DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation
inferred← citeshttps://doi.org/10.1177/0272989x19832914openalex-snapshot
A Probabilistic Model to Support Radiologists’ Classification Decisions in Mammography Practice
inferred← citeshttps://doi.org/10.1177/0272989x241264572openalex-snapshot
Modeling Radiologists’ Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms
inferred← citeshttps://doi.org/10.1177/08465371211031186openalex-snapshot
The Impact of Radiologist Screening Mammogram Reading Volume on Performance in the Ontario Breast Screening Program
inferred← citeshttps://doi.org/10.1177/0962280216643347openalex-snapshot
A measure of association for ordered categorical data in population-based studies
inferred← citeshttps://doi.org/10.1177/09691413251372829openalex-snapshot
An economic scenario analysis of implementing artificial intelligence in BreastScreen Norway–Impact on radiologist person-years, costs and effects
inferred← citeshttps://doi.org/10.1177/20584601211072279openalex-snapshot
Breast cancer detection across dense and non-dense breasts: Markers of diagnostic confidence and efficacy
inferred← citeshttps://doi.org/10.1186/s12889-017-4015-3openalex-snapshot
Performance of a subsidised mammographic screening programme in Malaysia, a middle-income Asian country
inferred← citeshttps://doi.org/10.1186/s13244-025-01922-wopenalex-snapshot
Annotation-efficient, patch-based, explainable deep learning using curriculum method for breast cancer detection in screening mammography
inferred← citeshttps://doi.org/10.1186/s13244-026-02323-3openalex-snapshot
An interpretable ultrasound-based deep learning system for early breast cancer in a Chinese population
inferred← citeshttps://doi.org/10.1245/s10434-014-4205-5openalex-snapshot
Breast Imaging Second Opinions Impact Surgical Management
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