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勘误：使用图像生物标志物和临床参数组合改进乳腺肿块良恶性预测。

Erratum: Improving the Prediction of Benign or Malignant Breast Masses Using a Combination of Image Biomarkers and Clinical Parameters.

出版信息

Front Oncol. 2021 Apr 29;11:694094. doi: 10.3389/fonc.2021.694094. eCollection 2021.

DOI:10.3389/fonc.2021.694094
PMID:33996613
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8117412/
Abstract

[This corrects the article DOI: 10.3389/fonc.2021.629321.].

摘要

[本文更正了文章的数字对象标识符：10.3389/fonc.2021.629321。]

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本文引用的文献

1
Deep Learning for Breast Cancer Diagnosis from Mammograms-A Comparative Study.基于乳房X光照片的深度学习乳腺癌诊断——一项对比研究
J Imaging. 2019 Mar 13;5(3):37. doi: 10.3390/jimaging5030037.
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Breast cancer detection using deep convolutional neural networks and support vector machines.使用深度卷积神经网络和支持向量机进行乳腺癌检测。
PeerJ. 2019 Jan 28;7:e6201. doi: 10.7717/peerj.6201. eCollection 2019.
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Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.基于不同成像模态的用于乳腺癌计算机辅助诊断的机器学习技术：系统综述。
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Texture-based classification of different single liver lesion based on SPAIR T2W MRI images.基于SPAIR T2W MRI图像的不同单一肝脏病变的纹理分类
BMC Med Imaging. 2017 Jul 13;17(1):42. doi: 10.1186/s12880-017-0212-x.
5
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance.基于交并比的深度全卷积网络自动皮肤病变分割。
IEEE Trans Med Imaging. 2017 Sep;36(9):1876-1886. doi: 10.1109/TMI.2017.2695227. Epub 2017 Apr 18.
6
Large scale deep learning for computer aided detection of mammographic lesions.基于大规模深度学习的计算机辅助乳腺病变检测
Med Image Anal. 2017 Jan;35:303-312. doi: 10.1016/j.media.2016.07.007. Epub 2016 Aug 2.
7
An evaluation of image descriptors combined with clinical data for breast cancer diagnosis.评估图像描述符与临床数据相结合在乳腺癌诊断中的应用。
Int J Comput Assist Radiol Surg. 2013 Jul;8(4):561-74. doi: 10.1007/s11548-013-0838-2. Epub 2013 Apr 13.
8
US of breast masses categorized as BI-RADS 3, 4, and 5: pictorial review of factors influencing clinical management.乳腺肿块的 US 分类为 BI-RADS 3、4 和 5：影响临床管理因素的影像学综述。
Radiographics. 2010 Sep;30(5):1199-213. doi: 10.1148/rg.305095144.
9
Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.基于互信息的特征选择：最大依赖、最大相关和最小冗余准则。
IEEE Trans Pattern Anal Mach Intell. 2005 Aug;27(8):1226-38. doi: 10.1109/TPAMI.2005.159.
10
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.比较两条或多条相关的受试者工作特征曲线下的面积：一种非参数方法。
Biometrics. 1988 Sep;44(3):837-45.