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• W4213043145 abstract "Journal of Magnetic Resonance ImagingVolume 56, Issue 4 p. 1230-1231 Editorial Editorial for “Anatomical Partition-Based Deep Learning: An Automatic Nasopharyngeal Magnetic Resonance Image Recognition Scheme” Eric K. van Staalduinen DO, Corresponding Author Eric K. van Staalduinen DO [email protected] orcid.org/0000-0001-9786-2712 Department of Radiology, Stanford University School of Medicine, Stanford, California, USASearch for more papers by this author Eric K. van Staalduinen DO, Corresponding Author Eric K. van Staalduinen DO [email protected] orcid.org/0000-0001-9786-2712 Department of Radiology, Stanford University School of Medicine, Stanford, California, USASearch for more papers by this author First published: 15 February 2022 https://doi.org/10.1002/jmri.28119 Level of Evidence: 5 Technical Efficacy Stage: 3 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. References 1Recht MP, Zbontar J, Sodickson DK, et al. Using deep learning to accelerate knee MRI at 3 T: Results of an interchangeability study. Am J Roentgenol 2020; 215(6): 1421- 1429. https://doi.org/10.2214/AJR.20.23313 2Chen DYT, Ishii Y, Fan AP, et al. Predicting PET cerebrovascular reserve with deep learning by using baseline MRI: A pilot investigation of a drug-free brain stress test. Radiology 2020; 296(3): 627- 637. https://doi.org/10.1148/radiol.2020192793 3Chang PD. Fully convolutional deep residual neural networks for brain tumor segmentation. In: A Crimi, B Menze, O Maier, M Reyes, S Winzeck, H Handels, editors. Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries: Cham: Springer International Publishing; 2016. p 108- 118. https://doi.org/10.1007/978-3-319-55524-9_11 4Rauschecker AM, Rudie JD, Xie L, et al. Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain MRI. Radiology 2020; 295(3): 626- 637. https://doi.org/10.1148/radiol.2020190283 5Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP. Deep learning in neuroradiology. Am J Neuroradiol 2018; 39(10): 1776- 1784. https://doi.org/10.3174/ajnr.A5543 6Li S, Hua HL, Li F, et al. Anatomical partition-based deep learning: An automatic nasopharyngeal MRI recognition scheme. J Magn Reson Imaging 2022; 56(4): 1220- 1229. 7Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015; 34(10): 1993- 2024. https://doi.org/10.1109/TMI.2014.2377694 8Harnsberger HR, Osborn AG. Differential diagnosis of head and neck lesions based on their space of origin. 1. The suprahyoid part of the neck. Am J Roentgenol 1991; 157(1): 147- 154. https://doi.org/10.2214/ajr.157.1.2048510 9Smoker WR, Harnsberger HR. Differential diagnosis of head and neck lesions based on their space of origin. 2. The infrahyoid portion of the neck. Am J Roentgenol 1991; 157(1): 155- 159. https://doi.org/10.2214/ajr.157.1.2048511 Volume56, Issue4October 2022Pages 1230-1231 ReferencesRelatedInformation" @default.
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• W4213043145 title "Editorial for “Anatomical <scp>Partition‐Based</scp> Deep Learning: An Automatic Nasopharyngeal Magnetic Resonance Image Recognition Scheme”" @default.
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