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10.1109/TCBB.2021.3102584 http://scihub22266oqcxt.onion/10.1109/TCBB.2021.3102584 34351863!9647725!34351863     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE/ACM+Trans+Comput+Biol+Bioinform 2022 ; 19 (5): 2723-2736 Nephropedia Template TP {{tp|p=34351863|t=2022. Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images |pdf=|usr=}}{{34351863}} gab.com Text Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images JO: IEEE/ACM Trans Comput Biol Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34351863 #FOAvip Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task. In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images. To fully explore features characterizing CT images from different frequency domains, DSAE was proposed to learn the latent representation by multi-task learning. The proposal was designed to both encode valuable information from different frequency features and construct a compact class structure for separability. To achieve this, we designed a multi-task loss function, which consists of a supervised loss and a reconstruction loss. Our proposed method was evaluated on a newly collected dataset of 787 subjects including COVID-19 pneumonia patients, other pneumonia patients, and normal subjects without abnormal CT findings. Extensive experimental results demonstrated that our proposed method achieved encouraging diagnostic performance and may have potential clinical application for the diagnosis of COVID-19. Twit Text FOAVip Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images ????IEEE/ACM Trans Comput Biol Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34351863 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34351863 #FOAvip English Wikipedia <ref>{{Cite pmid|34351863}}</ref> Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images #MMPMID34351863 Cheng J; Zhao W; Liu J; Xie X; Wu S; Liu L; Yue H; Li J; Wang J; Liu J IEEE/ACM Trans Comput Biol Bioinform 2022[Sep]; 19 (5): 2723-2736 PMID34351863show ga Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task. In this study, we proposed a deep supervised autoencoder (DSAE) framework to automatically identify COVID-19 using multi-view features extracted from CT images. To fully explore features characterizing CT images from different frequency domains, DSAE was proposed to learn the latent representation by multi-task learning. The proposal was designed to both encode valuable information from different frequency features and construct a compact class structure for separability. To achieve this, we designed a multi-task loss function, which consists of a supervised loss and a reconstruction loss. Our proposed method was evaluated on a newly collected dataset of 787 subjects including COVID-19 pneumonia patients, other pneumonia patients, and normal subjects without abnormal CT findings. Extensive experimental results demonstrated that our proposed method achieved encouraging diagnostic performance and may have potential clinical application for the diagnosis of COVID-19. |*COVID-19/diagnostic imaging[MESH] |*Deep Learning[MESH] |*Pneumonia[MESH] |COVID-19 Testing[MESH] |Humans[MESH] |SARS-CoV-2[MESH]Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With(epmc).pdf DeepDyve Pubget Overpricing