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For: Chaudhari AS, Sandino CM, Cole EK, Larson DB, Gold GE, Vasanawala SS, Lungren MP, Hargreaves BA, Langlotz CP. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. J Magn Reson Imaging 2021;54:357-71. [PMID: 32830874 DOI: 10.1002/jmri.27331] [Cited by in Crossref: 15] [Cited by in F6Publishing: 20] [Article Influence: 5.0] [Reference Citation Analysis]
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13 Chao F, Zhang Y, Byrd RA. Facilitating spectral analyses, simplification, and new tools through deep neural networks. Magnetic Resonance Letters 2022;2:56-58. [DOI: 10.1016/j.mrl.2021.12.001] [Reference Citation Analysis]
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16 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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19 Chaudhari AS, Mittra E, Davidzon GA, Gulaka P, Gandhi H, Brown A, Zhang T, Srinivas S, Gong E, Zaharchuk G, Jadvar H. Low-count whole-body PET with deep learning in a multicenter and externally validated study. NPJ Digit Med 2021;4:127. [PMID: 34426629 DOI: 10.1038/s41746-021-00497-2] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
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21 Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML). Diagnostics (Basel) 2021;11:742. [PMID: 33919342 DOI: 10.3390/diagnostics11050742] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
22 Desai AD, Caliva F, Iriondo C, Mortazi A, Jambawalikar S, Bagci U, Perslev M, Igel C, Dam EB, Gaj S, Yang M, Li X, Deniz CM, Juras V, Regatte R, Gold GE, Hargreaves BA, Pedoia V, Chaudhari AS; IWOAI Segmentation Challenge Writing Group. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiol Artif Intell 2021;3:e200078. [PMID: 34235438 DOI: 10.1148/ryai.2021200078] [Cited by in Crossref: 25] [Cited by in F6Publishing: 26] [Article Influence: 12.5] [Reference Citation Analysis]
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