勘误：用于疾病分类的机器学习与特征选择方法及其在肺癌筛查图像数据中的应用
Corrigendum: Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data.
作者信息
Delzell Darcie A P, Magnuson Sara, Peter Tabitha, Smith Michelle, Smith Brian J
机构信息
Department of Mathematics and Computer Science, Wheaton College, Wheaton, IL, United States.
Department of Biostatistics, University of Iowa, Iowa City, IA, United States.
出版信息
Front Oncol. 2020 Jun 5;10:866. doi: 10.3389/fonc.2020.00866. eCollection 2020.
Abstract
[This corrects the article DOI: 10.3389/fonc.2019.01393.].
摘要
[本文更正了文章的数字对象标识符（DOI）：10.3389/fonc.2019.01393。]
相似文献
1
Corrigendum: Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data.勘误：用于疾病分类的机器学习与特征选择方法及其在肺癌筛查图像数据中的应用
Front Oncol. 2020 Jun 5;10:866. doi: 10.3389/fonc.2020.00866. eCollection 2020.
2
Corrigendum: Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma.勘误：基于影像组学的纯磨玻璃结节定量CT分析以预测肺腺癌的侵袭性。
Front Oncol. 2020 Oct 30;10:608365. doi: 10.3389/fonc.2020.608365. eCollection 2020.
3
Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data.用于疾病分类的机器学习和特征选择方法及其在肺癌筛查图像数据中的应用
Front Oncol. 2019 Dec 11;9:1393. doi: 10.3389/fonc.2019.01393. eCollection 2019.
4
Corrigendum: Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.勘误：基于机器学习的磁共振影像组学分析用于胶质肉瘤和胶质母细胞瘤的分类
Front Oncol. 2021 Oct 18;11:774369. doi: 10.3389/fonc.2021.774369. eCollection 2021.
5
Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.基于影像组学特征提取和机器学习的局部薄层 CT 对肺癌筛查中早期检出肺结节的分类
Phys Med Biol. 2018 Mar 14;63(6):065005. doi: 10.1088/1361-6560/aaafab.
6
Corrigendum: A Non-invasive Method to Diagnose Lung Adenocarcinoma.勘误：一种诊断肺腺癌的非侵入性方法。
Front Oncol. 2020 Aug 20;10:1515. doi: 10.3389/fonc.2020.01515. eCollection 2020.
7
Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.基于机器学习的放射组学策略预测非小细胞肺癌细胞增殖。
Eur J Radiol. 2019 Sep;118:32-37. doi: 10.1016/j.ejrad.2019.06.025. Epub 2019 Jun 28.
8
Prediction of pathologic stage in non-small cell lung cancer using machine learning algorithm based on CT image feature analysis.基于 CT 图像特征分析的机器学习算法预测非小细胞肺癌病理分期。
BMC Cancer. 2019 May 17;19(1):464. doi: 10.1186/s12885-019-5646-9.
9
Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.勘误：头颈癌治疗结果预测：基于传统影像组学特征的机器学习与深度学习影像组学的比较
Front Med (Lausanne). 2024 May 14;11:1421603. doi: 10.3389/fmed.2024.1421603. eCollection 2024.
10
Corrigendum: Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.勘误：基于临床放射组学的机器学习与三维卷积神经网络分析在胸部计算机断层扫描上鉴别胸腺上皮肿瘤与其他前纵隔肿瘤的比较。
Front Oncol. 2023 May 31;13:1220962. doi: 10.3389/fonc.2023.1220962. eCollection 2023.
Zotero 插件