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Cited by in F6Publishing
For: Luo Y, Tseng HH, Cui S, Wei L, Ten Haken RK, El Naqa I. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR Open. 2019;1:20190021. [PMID: 33178948 DOI: 10.1259/bjro.20190021] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
Number Citing Articles
1 Schelling X, Fernández J, Ward P, Fernández J, Robertson S. Decision Support System Applications for Scheduling in Professional Team Sport. The Team's Perspective. Front Sports Act Living 2021;3:678489. [PMID: 34151262 DOI: 10.3389/fspor.2021.678489] [Reference Citation Analysis]
2 Cui S, Ten Haken RK, El Naqa I. Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy. Int J Radiat Oncol Biol Phys 2021;110:893-904. [PMID: 33539966 DOI: 10.1016/j.ijrobp.2021.01.042] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Boyle R, Jollans L, Rueda-Delgado LM, Rizzo R, Yener GG, McMorrow JP, Knight SP, Carey D, Robertson IH, Emek-Savaş DD, Stern Y, Kenny RA, Whelan R. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging Behav 2021;15:327-45. [PMID: 32141032 DOI: 10.1007/s11682-020-00260-3] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 11.0] [Reference Citation Analysis]
4 Barragán-Montero A, Javaid U, Valdés G, Nguyen D, Desbordes P, Macq B, Willems S, Vandewinckele L, Holmström M, Löfman F, Michiels S, Souris K, Sterpin E, Lee JA. Artificial intelligence and machine learning for medical imaging: A technology review. Phys Med 2021;83:242-56. [PMID: 33979715 DOI: 10.1016/j.ejmp.2021.04.016] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021;8:031902. [PMID: 33768134 DOI: 10.1117/1.JMI.8.3.031902] [Reference Citation Analysis]
6 Weller DL, Love TMT, Wiedmann M. Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water. Front Artif Intell 2021;4:628441. [PMID: 34056577 DOI: 10.3389/frai.2021.628441] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Luo W. Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning. Front Artif Intell 2021;4:627369. [PMID: 34164615 DOI: 10.3389/frai.2021.627369] [Reference Citation Analysis]
8 Ip WY, Yeung FK, Yung SPF, Yu HCJ, So TH, Vardhanabhuti V. Current landscape and potential future applications of artificial intelligence in medical physics and radiotherapy. Artif Intell Med Imaging 2021; 2(2): 37-55 [DOI: 10.35711/aimi.v2.i2.37] [Reference Citation Analysis]
9 Kazmierska J, Hope A, Spezi E, Beddar S, Nailon WH, Osong B, Ankolekar A, Choudhury A, Dekker A, Redalen KR, Traverso A. From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community. Radiother Oncol 2020;153:43-54. [PMID: 33065188 DOI: 10.1016/j.radonc.2020.09.054] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
10 Balagurunathan Y, Mitchell R, El Naqa I. Requirements and reliability of AI in the medical context. Phys Med 2021;83:72-8. [PMID: 33721700 DOI: 10.1016/j.ejmp.2021.02.024] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]