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10.1016/j.patcog.2021.108109 http://scihub22266oqcxt.onion/10.1016/j.patcog.2021.108109 34127870!8189738!34127870     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Recognit 2021 ; 119 (ä): 108109 Nephropedia Template TP {{tp|p=34127870|t=2021. SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images |pdf=|usr=}}{{34127870}} gab.com Text SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images JO: Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=34127870 #FOAvip Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability. Twit Text FOAVip SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images ????Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=34127870 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34127870 #FOAvip English Wikipedia <ref>{{Cite pmid|34127870}}</ref> SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images #MMPMID34127870 Zhao S; Li Z; Chen Y; Zhao W; Xie X; Liu J; Zhao D; Li Y Pattern Recognit 2021[Nov]; 119 (ä): 108109 PMID34127870show ga Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability. äSCOAT-Net: A novel network for segmenting COVID-19 lung opacification (epmc).pdf DeepDyve Pubget Overpricing