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Cited by in F6Publishing
For: Sveinsson B, Chaudhari AS, Zhu B, Koonjoo N, Torriani M, Gold GE, Rosen MS. Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network. Radiol Artif Intell 2021;3:e200122. [PMID: 34617020 DOI: 10.1148/ryai.2021200122] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Huang C, Qian Y, Yu SC, Hou J, Jiang B, Chan Q, Wong VW, Chu WC, Chen W. Uncertainty-aware self-supervised neural network for liverT(1ρ)mapping with relaxation constraint. Phys Med Biol 2022;67. [PMID: 36317270 DOI: 10.1088/1361-6560/ac9e3e] [Reference Citation Analysis]
2 Becker AS. Evolution of deep learning trends between 2012 and 2020: A perspective from the EJR editorial board. Eur J Radiol 2022;155:110462. [PMID: 35964507 DOI: 10.1016/j.ejrad.2022.110462] [Reference Citation Analysis]
3 Raman S, Gold GE, Rosen MS, Sveinsson B. Automatic estimation of knee effusion from limited MRI data. Sci Rep 2022;12:3155. [PMID: 35210490 DOI: 10.1038/s41598-022-07092-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
4 Gokyar S, Robb FJL, Kainz W, Chaudhari A, Winkler SA. MRSaiFE: An AI-based Approach Towards the Real-Time Prediction of Specific Absorption Rate. IEEE Access 2021;9:140824-34. [PMID: 34722096 DOI: 10.1109/access.2021.3118290] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
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