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10.1109/TMI.2020.2993291 http://scihub22266oqcxt.onion/10.1109/TMI.2020.2993291 32396075!ä!32396075 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       IEEE+Trans+Med+Imaging 2020 ; 39 (8): 2688-2700 Nephropedia Template TP {{tp|p=32396075|t=2020. Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets |pdf=|usr=}}{{32396075}} gab.com Text Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32396075 #FOAvip Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. Twit Text FOAVip Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32396075 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32396075 #FOAvip English Wikipedia <ref>{{Cite pmid|32396075}}</ref> Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets #MMPMID32396075 Oh Y; Park S; Ye JC IEEE Trans Med Imaging 2020[Aug]; 39 (8): 2688-2700 PMID32396075show ga Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. |*Deep Learning[MESH] |Algorithms[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Lung/diagnostic imaging[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Radiography, Thoracic/*methods[MESH]Deep Learning COVID-19 Features on CXR Using Limited Training Data Set(epmc).pdf DeepDyve Pubget Overpricing