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本文引用的文献

1
Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.基于堆叠泛化分类器的数字乳腺X线摄影中微钙化簇的分类
J Imaging. 2019 Sep 12;5(9):76. doi: 10.3390/jimaging5090076.
2
Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study.定量乳腺密度分析预测间隔期和淋巴结阳性癌症以改进筛查方案:病例对照研究。
Br J Cancer. 2021 Sep;125(6):884-892. doi: 10.1038/s41416-021-01466-y. Epub 2021 Jun 24.
3
Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study.人工智能在乳腺癌检测和假阳性召回中的变化:一项回顾性、多读者研究。
Lancet Digit Health. 2020 Mar;2(3):e138-e148. doi: 10.1016/S2589-7500(20)30003-0. Epub 2020 Feb 6.
4
Deep learning for mass detection in Full Field Digital Mammograms.深度学习用于全视野数字乳腺X线摄影中的肿块检测。
Comput Biol Med. 2020 Jun;121:103774. doi: 10.1016/j.compbiomed.2020.103774. Epub 2020 Apr 22.
5
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.
6
Image processing can cause some malignant soft-tissue lesions to be missed in digital mammography images.图像处理可能导致在数字乳腺X线摄影图像中遗漏一些恶性软组织病变。
Clin Radiol. 2017 Sep;72(9):799.e1-799.e8. doi: 10.1016/j.crad.2017.03.024. Epub 2017 Apr 27.
7
MEDXVIEWER: PROVIDING A WEB-ENABLED WORKSTATION ENVIRONMENT FOR COLLABORATIVE AND REMOTE MEDICAL IMAGING VIEWING, PERCEPTION STUDIES AND READER TRAINING.MEDXVIEWER:提供一个支持网络的工作站环境,用于协作和远程医学影像查看、感知研究及读者培训。
Radiat Prot Dosimetry. 2016 Jun;169(1-4):32-7. doi: 10.1093/rpd/ncv482. Epub 2015 Nov 30.
8
Breast cancer detection rates using four different types of mammography detectors.使用四种不同类型的乳腺摄影探测器的乳腺癌检测率。
Eur Radiol. 2016 Mar;26(3):874-83. doi: 10.1007/s00330-015-3885-y. Epub 2015 Jun 25.

OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data.

作者信息

Halling-Brown Mark D, Warren Lucy M, Ward Dominic, Lewis Emma, Mackenzie Alistair, Wallis Matthew G, Wilkinson Louise S, Given-Wilson Rosalind M, McAvinchey Rita, Young Kenneth C

机构信息

Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George's Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.).

出版信息

Radiol Artif Intell. 2020 Nov 25;3(1):e200103. doi: 10.1148/ryai.2020200103. eCollection 2021 Jan.

DOI:10.1148/ryai.2020200103
PMID:33937853
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8082293/
Abstract
摘要