埃默里乳腺成像数据集（EMBED）：一个包含340万张筛查和诊断性乳腺钼靶图像的种族多样化、详细的数据集。
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.
作者信息
Jeong Jiwoong J, Vey Brianna L, Bhimireddy Ananth, Kim Thomas, Santos Thiago, Correa Ramon, Dutt Raman, Mosunjac Marina, Oprea-Ilies Gabriela, Smith Geoffrey, Woo Minjae, McAdams Christopher R, Newell Mary S, Banerjee Imon, Gichoya Judy, Trivedi Hari
机构信息
School of Computing and Augmented Intelligence, Arizona State University, 699 S Mill Ave, Tempe, AZ 85281 (J.J.J., R.C., I.B.); Departments of Radiology (B.L.V., A.B., R.D., C.R.M., M.S.N., J.G., H.T.), Computer Science (T.S.), and Pathology (M.M., G.O., G.S.), Emory University, Atlanta, Ga; College of Computing, Georgia Institute of Technology, Atlanta, Ga (T.K.); and School of Data Science and Analytics, Kennesaw State University, Kennesaw, Ga (M.W.).
出版信息
Radiol Artif Intell. 2023 Jan 4;5(1):e220047. doi: 10.1148/ryai.220047. eCollection 2023 Jan.
Abstract
Mammography, Breast, Machine Learning © RSNA, 2023.
摘要
乳腺钼靶摄影、乳房、机器学习 © RSNA，2023 年。
相似文献
1
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.埃默里乳腺成像数据集（EMBED）：一个包含340万张筛查和诊断性乳腺钼靶图像的种族多样化、详细的数据集。
Radiol Artif Intell. 2023 Jan 4;5(1):e220047. doi: 10.1148/ryai.220047. eCollection 2023 Jan.
2
A Multi-million Mammography Image Dataset and Population-Based Screening Cohort for the Training and Evaluation of Deep Neural Networks-the Cohort of Screen-Aged Women (CSAW).一个包含数百万张乳腺 X 光图像的数据集和一个基于人群的筛查队列，用于深度学习神经网络的培训和评估 - 筛查年龄段女性队列（CSAW）。
J Digit Imaging. 2020 Apr;33(2):408-413. doi: 10.1007/s10278-019-00278-0.
3
Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus.基于放射科医生共识的人工智能驱动的乳腺钼靶图像乳腺密度分类工具的开发与验证
Radiol Artif Intell. 2022 Mar 16;4(2):e210199. doi: 10.1148/ryai.210199. eCollection 2022 Mar.
4
A deep learning method for classifying mammographic breast density categories.一种用于对乳腺钼靶图像的乳房密度类别进行分类的深度学习方法。
Med Phys. 2018 Jan;45(1):314-321. doi: 10.1002/mp.12683. Epub 2017 Dec 22.
5
Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification.基于区域池化结构的深度神经网络在乳腺图像分类中的应用。
IEEE Trans Med Imaging. 2020 Jun;39(6):2246-2255. doi: 10.1109/TMI.2020.2968397. Epub 2020 Jan 21.
6
A Deep Learning Approach to Re-create Raw Full-Field Digital Mammograms for Breast Density and Texture Analysis.一种用于重新创建原始全场数字化乳腺X线摄影图像以进行乳腺密度和纹理分析的深度学习方法。
Radiol Artif Intell. 2021 Apr 14;3(4):e200097. doi: 10.1148/ryai.2021200097. eCollection 2021 Jul.
7
Mammographic Breast Density, Benign Breast Disease, and Subsequent Breast Cancer Risk in 3.9 Million Korean Women.390万韩国女性的乳腺钼靶密度、良性乳腺疾病与后续乳腺癌风险
Radiology. 2022 Sep;304(3):534-541. doi: 10.1148/radiol.212727. Epub 2022 May 17.
8
Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets.基于特征提取技术的有限乳腺成像数据集的迁移学习增强。
J Digit Imaging. 2021 Jun;34(3):618-629. doi: 10.1007/s10278-021-00456-z. Epub 2021 May 10.
9
The supplemental value of mammographic screening over breast MRI alone in BRCA2 mutation carriers.乳腺钼靶筛查联合乳腺 MRI 检查相较于单独进行乳腺 MRI 检查对 BRCA2 基因突变携带者的附加价值。
Breast Cancer Res Treat. 2020 Jun;181(3):581-588. doi: 10.1007/s10549-020-05642-1. Epub 2020 Apr 24.
10
Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks.基于深度学习的 2D 乳腺 X 光图像语义标注及机器学习任务复杂度比较。
J Digit Imaging. 2019 Aug;32(4):565-570. doi: 10.1007/s10278-019-00244-w.
引用本文的文献
1
Full Field Digital Mammography Dataset from a Population Screening Program.来自人群筛查项目的全视野数字乳腺摄影数据集。
Sci Data. 2025 Aug 25;12(1):1479. doi: 10.1038/s41597-025-05866-0.
2
Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review.人工智能在胰腺导管内乳头状黏液性肿瘤成像中的应用：一项系统综述。
PLOS Digit Health. 2025 Jul 23;4(7):e0000920. doi: 10.1371/journal.pdig.0000920. eCollection 2025 Jul.
3
Benchmarking Distance Functions in Siamese Networks for Current and Prior Mammogram Image Analysis.用于当前和先前乳房X光图像分析的暹罗网络中的基准距离函数
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2024 Dec;2024:1996-2003. doi: 10.1109/bibm62325.2024.10822291.
4
Multi-site validation of an interpretable model to analyze breast masses.用于分析乳腺肿块的可解释模型的多中心验证
PLoS One. 2025 Jun 26;20(6):e0320091. doi: 10.1371/journal.pone.0320091. eCollection 2025.
5
Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography.亚组评估以了解基于深度学习的乳腺钼靶摄影感兴趣区域分类中的性能差距。
PLOS Digit Health. 2025 Apr 8;4(4):e0000811. doi: 10.1371/journal.pdig.0000811. eCollection 2025 Apr.
6
Deriving a Mammogram-Based Risk Score from Screening Digital Breast Tomosynthesis for 5-Year Breast Cancer Risk Prediction.从筛查数字乳腺断层合成中得出基于乳房X光检查的风险评分以预测5年乳腺癌风险
Cancer Prev Res (Phila). 2025 Jun 2;18(6):347-354. doi: 10.1158/1940-6207.CAPR-24-0427.
7
Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.冻结的大规模预训练视觉语言模型是多模态乳腺癌预测的有效基础骨干。
IEEE J Biomed Health Inform. 2025 May;29(5):3234-3246. doi: 10.1109/JBHI.2024.3507638. Epub 2025 May 6.
8
Foundation Models in Radiology: What, How, Why, and Why Not.放射学中的基础模型：是什么、如何、为何以及为何不。
Radiology. 2025 Feb;314(2):e240597. doi: 10.1148/radiol.240597.
9
Artificial Intelligence in Pancreatic Intraductal Papillary Mucinous Neoplasm Imaging: A Systematic Review.人工智能在胰腺导管内乳头状黏液性肿瘤成像中的应用：一项系统综述
medRxiv. 2025 Jan 9:2025.01.08.25320130. doi: 10.1101/2025.01.08.25320130.
10
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning.CLEFT：基于高效大语言模型和提示微调的语言-图像对比学习
Med Image Comput Comput Assist Interv. 2024 Oct;15012:465-475. doi: 10.1007/978-3-031-72390-2_44. Epub 2024 Oct 23.
本文引用的文献
1
Optimizing risk-based breast cancer screening policies with reinforcement learning.基于强化学习优化基于风险的乳腺癌筛查政策。
Nat Med. 2022 Jan;28(1):136-143. doi: 10.1038/s41591-021-01599-w. Epub 2022 Jan 13.
2
Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model.基于乳腺 X 线摄影的乳腺癌风险模型的多机构验证。
J Clin Oncol. 2022 Jun 1;40(16):1732-1740. doi: 10.1200/JCO.21.01337. Epub 2021 Nov 12.
3
A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images.针对实时放射图像的机器学习和处理管道的 DICOM 框架。
J Digit Imaging. 2021 Aug;34(4):1005-1013. doi: 10.1007/s10278-021-00491-w. Epub 2021 Aug 17.
4
A Review of Applications of Machine Learning in Mammography and Future Challenges.机器学习在乳腺 X 线摄影中的应用综述及未来挑战
Oncology. 2021;99(8):483-490. doi: 10.1159/000515698. Epub 2021 May 21.
5
OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data.OPTIMAM乳腺X线摄影图像数据库：乳腺X线摄影图像和临床数据的大规模资源。
Radiol Artif Intell. 2020 Nov 25;3(1):e200103. doi: 10.1148/ryai.2020200103. eCollection 2021 Jan.
6
The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings.放射学人工智能现状：购买决策考量与当前市场产品
Radiol Artif Intell. 2020 Nov 11;2(6):e200004. doi: 10.1148/ryai.2020200004. eCollection 2020 Nov.
7
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.联合人工智能和放射科医生评估解读筛查性乳房 X 光照片的效果。
JAMA Netw Open. 2020 Mar 2;3(3):e200265. doi: 10.1001/jamanetworkopen.2020.0265.
8
Can we trust AI not to further embed racial bias and prejudice?我们能相信人工智能不会进一步强化种族偏见吗？
BMJ. 2020 Feb 12;368:m363. doi: 10.1136/bmj.m363.
9
International evaluation of an AI system for breast cancer screening.国际乳腺癌筛查人工智能系统评估。
Nature. 2020 Jan;577(7788):89-94. doi: 10.1038/s41586-019-1799-6. Epub 2020 Jan 1.
10
Dissecting racial bias in an algorithm used to manage the health of populations.剖析用于管理人群健康的算法中的种族偏见。
Science. 2019 Oct 25;366(6464):447-453. doi: 10.1126/science.aax2342.
Zotero 插件