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10.1016/j.compbiomed.2020.104092 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2020.104092 33161334!7591316!33161334     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Biol+Med 2020 ; 127 (ä): 104092 Nephropedia Template TP {{tp|p=33161334|t=2020. The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose |pdf=|usr=}}{{33161334}} gab.com Text The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33161334 #FOAvip With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using CT and radiograph images of patients' lungs. The two most popular publicly available datasets for COVID-19 classification are COVID-CT and COVID-19 Image Data Collection. In this work, we propose a new dataset which we call COVID-19 CT & Radiograph Image Data Stock. It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. Moreover, it is supplemented with a carefully defined split. The aim of COVID-19 CT & Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. We hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models. Twit Text FOAVip The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33161334 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33161334 #FOAvip English Wikipedia <ref>{{Cite pmid|33161334}}</ref> The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose #MMPMID33161334 Misztal K; Pocha A; Durak-Kozica M; Wator M; Kubica-Misztal A; Hartel M Comput Biol Med 2020[Dec]; 127 (ä): 104092 PMID33161334show ga With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using CT and radiograph images of patients' lungs. The two most popular publicly available datasets for COVID-19 classification are COVID-CT and COVID-19 Image Data Collection. In this work, we propose a new dataset which we call COVID-19 CT & Radiograph Image Data Stock. It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. Moreover, it is supplemented with a carefully defined split. The aim of COVID-19 CT & Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. We hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models. |*Deep Learning[MESH] |COVID-19/*diagnostic imaging/virology[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |SARS-CoV-2/isolation & purification[MESH]The importance of standardisation - COVID-19 CT & Radiograph Image Dat(epmc).pdf DeepDyve Pubget Overpricing