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10.1117/1.JMI.8.S1.014501 http://scihub22266oqcxt.onion/10.1117/1.JMI.8.S1.014501 33415179!7773028!33415179     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Med+Imaging+(Bellingham) 2021 ; 8 (Suppl 1): 014501 Nephropedia Template TP {{tp|p=33415179|t=2021. Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids |pdf=|usr=}}{{33415179}} gab.com Text Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids JO: J Med Imaging (Bellingham) http://www.kidney.de/pget.php?&tw=1&pmid=33415179 #FOAvip Purpose: Given the recent COVID-19 pandemic and its stress on global medical resources, presented here is the development of a machine intelligent method for thoracic computed tomography (CT) to inform management of patients on steroid treatment. Approach: Transfer learning has demonstrated strong performance when applied to medical imaging, particularly when only limited data are available. A cascaded transfer learning approach extracted quantitative features from thoracic CT sections using a fine-tuned VGG19 network. The extracted slice features were axially pooled to provide a CT-scan-level representation of thoracic characteristics and a support vector machine was trained to distinguish between patients who required steroid administration and those who did not, with performance evaluated through receiver operating characteristic (ROC) curve analysis. Least-squares fitting was used to assess temporal trends using the transfer learning approach, providing a preliminary method for monitoring disease progression. Results: In the task of identifying patients who should receive steroid treatments, this approach yielded an area under the ROC curve of 0.85 +/- 0.10 and demonstrated significant separation between patients who received steroids and those who did not. Furthermore, temporal trend analysis of the prediction score matched expected progression during hospitalization for both groups, with separation at early timepoints prior to convergence near the end of the duration of hospitalization. Conclusions: The proposed cascade deep learning method has strong clinical potential for informing clinical decision-making and monitoring patient treatment. Twit Text FOAVip Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids ????J Med Imaging (Bellingham) http://www.kidney.de/pget.php?&tw=1&pmid=33415179 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33415179 #FOAvip English Wikipedia <ref>{{Cite pmid|33415179}}</ref> Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids #MMPMID33415179 Fuhrman JD; Chen J; Dong Z; Lure FYM; Luo Z; Giger ML J Med Imaging (Bellingham) 2021[Jan]; 8 (Suppl 1): 014501 PMID33415179show ga Purpose: Given the recent COVID-19 pandemic and its stress on global medical resources, presented here is the development of a machine intelligent method for thoracic computed tomography (CT) to inform management of patients on steroid treatment. Approach: Transfer learning has demonstrated strong performance when applied to medical imaging, particularly when only limited data are available. A cascaded transfer learning approach extracted quantitative features from thoracic CT sections using a fine-tuned VGG19 network. The extracted slice features were axially pooled to provide a CT-scan-level representation of thoracic characteristics and a support vector machine was trained to distinguish between patients who required steroid administration and those who did not, with performance evaluated through receiver operating characteristic (ROC) curve analysis. Least-squares fitting was used to assess temporal trends using the transfer learning approach, providing a preliminary method for monitoring disease progression. Results: In the task of identifying patients who should receive steroid treatments, this approach yielded an area under the ROC curve of 0.85 +/- 0.10 and demonstrated significant separation between patients who received steroids and those who did not. Furthermore, temporal trend analysis of the prediction score matched expected progression during hospitalization for both groups, with separation at early timepoints prior to convergence near the end of the duration of hospitalization. Conclusions: The proposed cascade deep learning method has strong clinical potential for informing clinical decision-making and monitoring patient treatment. äCascaded deep transfer learning on thoracic CT in COVID-19 patients tr(epmc).pdf DeepDyve Pubget Overpricing