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10.1109/JBHI.2020.3012383 http://scihub22266oqcxt.onion/10.1109/JBHI.2020.3012383 32750973!8545159!32750973     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 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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2020 ; 24 (10): 2776-2786 Nephropedia Template TP {{tp|p=32750973|t=2020. Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models |pdf=|usr=}}{{32750973}} gab.com Text Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=32750973 #FOAvip Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available. Twit Text FOAVip Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=32750973 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32750973 #FOAvip English Wikipedia <ref>{{Cite pmid|32750973}}</ref> Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models #MMPMID32750973 Bridge J; Meng Y; Zhao Y; Du Y; Zhao M; Sun R; Zheng Y IEEE J Biomed Health Inform 2020[Oct]; 24 (10): 2776-2786 PMID32750973show ga Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available. |*Algorithms[MESH] |*Betacoronavirus[MESH] |*Pandemics[MESH] |Bayes Theorem[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/*methods/statistics & numerical data[MESH] |Computational Biology[MESH] |Coronavirus Infections/*diagnosis/diagnostic imaging/epidemiology[MESH] |Databases, Factual/statistics & numerical data[MESH] |Deep Learning[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |Pneumonia, Viral/*diagnosis/diagnostic imaging/epidemiology[MESH] |Radiographic Image Interpretation, Computer-Assisted/methods[MESH] |SARS-CoV-2[MESH]Introducing the GEV Activation Function for Highly Unbalanced Data to (epmc).pdf DeepDyve Pubget Overpricing