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Cited by in F6Publishing
For: Qiu J, Peng S, Yin J, Wang J, Jiang J, Li Z, Song H, Zhang W. A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions. Interdiscip Sci 2021;13:61-72. [PMID: 33411162 DOI: 10.1007/s12539-020-00410-7] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 9.0] [Reference Citation Analysis]
Number Citing Articles
1 Moradi Khaniabadi P, Bouchareb Y, Al-Dhuhli H, Shiri I, Al-Kindi F, Moradi Khaniabadi B, Zaidi H, Rahmim A. Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics. Comput Biol Med 2022;150:106165. [PMID: 36215849 DOI: 10.1016/j.compbiomed.2022.106165] [Reference Citation Analysis]
2 Truong HM, Huynh HT. A Novel Approach For CT-Based COVID-19 Classification and Lesion Segmentation Based On Deep Learning. The Computer Journal 2022. [DOI: 10.1093/comjnl/bxac015] [Reference Citation Analysis]
3 Tillaguango Jiménez JR. Revisión Sistemática de Literatura: Análisis de viabilidad para la detección y diagnóstico de Covid-19, aplicando modelos de Inteligencia Artificial (IA). CEDAMAZ 2021;11:142-151. [DOI: 10.54753/cedamaz.v11i2.1183] [Reference Citation Analysis]
4 Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khateri M, Bijari S, Atashzar MR, Shayesteh SP, Khosravi B, Babaei MR, Jenabi E, Hasanian M, Shahhamzeh A, Gholami SYF, Mozafari A, Teimouri A, Movaseghi F, Ahmari A, Goharpey N, Bozorgmehr R, Shirzad-aski H, Mortazavi R, Karimi J, Mortazavi N, Besharat S, Afsharpad M, Abdollahi H, Geramifar P, Radmard AR, Arabi H, Rezaei-kalantari K, Oveisi M, Rahmim A, Zaidi H. COVID-19 Prognostic Modeling Using CT Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients.. [DOI: 10.1101/2021.12.07.21267364] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
5 Yousef HA, Moussa EMM, Abdel-razek MZM, El-kholy MMSA, Hasan LHS, El-sayed AEA, Saleh MAK, Omar MKM. Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation. Egypt J Radiol Nucl Med 2021;52:293. [DOI: 10.1186/s43055-021-00602-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Xu Z, Zhao L, Yang G, Ren Y, Wu J, Xia Y, Yang X, Cao M, Zhang G, Peng T, Zhao J, Yang H, Hu J, Du J. Severity Assessment of COVID-19 Using a CT-Based Radiomics Model. Stem Cells Int 2021;2021:2263469. [PMID: 34594383 DOI: 10.1155/2021/2263469] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
7 Yang N, Liu F, Li C, Xiao W, Xie S, Yuan S, Zuo W, Ma X, Jiang G. Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images. Sci Rep 2021;11:17885. [PMID: 34504246 DOI: 10.1038/s41598-021-97497-9] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
8 Yang F, Wan Y, Xu L, Wu Y, Shen X, Wang J, Lu D, Shao C, Zheng S, Niu T, Xu X. MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study. Front Oncol 2021;11:672126. [PMID: 34476208 DOI: 10.3389/fonc.2021.672126] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021;11:1317. [PMID: 34441252 DOI: 10.3390/diagnostics11081317] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
10 Jin Y, Junren W, Jingwen J, Yajing S, Xi C, Ke Q. Research on the Construction and Application of Breast Cancer-Specific Database System Based on Full Data Lifecycle. Front Public Health 2021;9:712827. [PMID: 34322474 DOI: 10.3389/fpubh.2021.712827] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]