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10.1049/ipr2.12249 http://scihub22266oqcxt.onion/10.1049/ipr2.12249 34226836!8242907!34226836     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IET+Image+Process 2021 ; 15 (11): 2604-2613 Nephropedia Template TP {{tp|p=34226836|t=2021. A multi-class COVID-19 segmentation network with pyramid attention and edge loss in CT images |pdf=|usr=}}{{34226836}} gab.com Text A multi-class COVID-19 segmentation network with pyramid attention and edge loss in CT images JO: IET Image Process http://www.kidney.de/pget.php?&tw=1&pmid=34226836 #FOAvip At the end of 2019, a novel coronavirus COVID-19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID-19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi-class COVID-19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi-scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms: ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID-SemiSeg is also evaluated. The results demonstrate that this model outperforms other state-of-the-art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field. Twit Text FOAVip A multi-class COVID-19 segmentation network with pyramid attention and edge loss in CT images ????IET Image Process http://www.kidney.de/pget.php?&tw=1&pmid=34226836 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34226836 #FOAvip English Wikipedia <ref>{{Cite pmid|34226836}}</ref> A multi-class COVID-19 segmentation network with pyramid attention and edge loss in CT images #MMPMID34226836 Yu F; Zhu Y; Qin X; Xin Y; Yang D; Xu T IET Image Process 2021[Sep]; 15 (11): 2604-2613 PMID34226836show ga At the end of 2019, a novel coronavirus COVID-19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID-19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi-class COVID-19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi-scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms: ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID-SemiSeg is also evaluated. The results demonstrate that this model outperforms other state-of-the-art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field. äA multi-class COVID-19 segmentation network with pyramid attention and(epmc).pdf DeepDyve Pubget Overpricing