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10.1016/j.patcog.2021.108006 http://scihub22266oqcxt.onion/10.1016/j.patcog.2021.108006 34002101!8116317!34002101     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Recognit 2021 ; 118 (ä): 108006 Nephropedia Template TP {{tp|p=34002101|t=2021. Joint segmentation and detection of COVID-19 via a sequential region generation network |pdf=|usr=}}{{34002101}} gab.com Text Joint segmentation and detection of COVID-19 via a sequential region generation network JO: Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=34002101 #FOAvip The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database. Twit Text FOAVip Joint segmentation and detection of COVID-19 via a sequential region generation network ????Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=34002101 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34002101 #FOAvip English Wikipedia <ref>{{Cite pmid|34002101}}</ref> Joint segmentation and detection of COVID-19 via a sequential region generation network #MMPMID34002101 Wu J; Zhang S; Li X; Chen J; Xu H; Zheng J; Gao Y; Tian Y; Liang Y; Ji R Pattern Recognit 2021[Oct]; 118 (ä): 108006 PMID34002101show ga The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database. äJoint segmentation and detection of COVID-19 via a sequential region g(epmc).pdf DeepDyve Pubget Overpricing