Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1016/j.patcog.2021.108168 http://scihub22266oqcxt.onion/10.1016/j.patcog.2021.108168 34305181!8272691!34305181     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Recognit 2021 ; 120 (ä): 108168 Nephropedia Template TP {{tp|p=34305181|t=2021. Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images |pdf=|usr=}}{{34305181}} gab.com Text Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images JO: Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=34305181 #FOAvip In this paper, a progressive global perception and local polishing (PCPLP) network is proposed to automatically segment the COVID-19-caused pneumonia infections in computed tomography (CT) images. The proposed PCPLP follows an encoder-decoder architecture. Particularly, the encoder is implemented as a computationally efficient fully convolutional network (FCN). In this study, a multi-scale multi-level feature recursive aggregation (mmFRA) network is used to integrate multi-scale features (viz. global guidance features and local refinement features) with multi-level features (viz. high-level semantic features, middle-level comprehensive features, and low-level detailed features). Because of this innovative aggregation of features, an edge-preserving segmentation map can be produced in a boundary-aware multiple supervision (BMS) way. Furthermore, both global perception and local perception are devised. On the one hand, a global perception module (GPM) providing a holistic estimation of potential lung infection regions is employed to capture more complementary coarse-structure information from different pyramid levels by enlarging the receptive fields without substantially increasing the computational burden. On the other hand, a local polishing module (LPM), which provides a fine prediction of the segmentation regions, is applied to explicitly heighten the fine-detail information and reduce the dilution effect of boundary knowledge. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed PCPLP in boosting the learning ability to identify the lung infected regions with clear contours accurately. Our model is superior remarkably to the state-of-the-art segmentation models both quantitatively and qualitatively on a real CT dataset of COVID-19. Twit Text FOAVip Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images ????Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=34305181 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34305181 #FOAvip English Wikipedia <ref>{{Cite pmid|34305181}}</ref> Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images #MMPMID34305181 Mu N; Wang H; Zhang Y; Jiang J; Tang J Pattern Recognit 2021[Dec]; 120 (ä): 108168 PMID34305181show ga In this paper, a progressive global perception and local polishing (PCPLP) network is proposed to automatically segment the COVID-19-caused pneumonia infections in computed tomography (CT) images. The proposed PCPLP follows an encoder-decoder architecture. Particularly, the encoder is implemented as a computationally efficient fully convolutional network (FCN). In this study, a multi-scale multi-level feature recursive aggregation (mmFRA) network is used to integrate multi-scale features (viz. global guidance features and local refinement features) with multi-level features (viz. high-level semantic features, middle-level comprehensive features, and low-level detailed features). Because of this innovative aggregation of features, an edge-preserving segmentation map can be produced in a boundary-aware multiple supervision (BMS) way. Furthermore, both global perception and local perception are devised. On the one hand, a global perception module (GPM) providing a holistic estimation of potential lung infection regions is employed to capture more complementary coarse-structure information from different pyramid levels by enlarging the receptive fields without substantially increasing the computational burden. On the other hand, a local polishing module (LPM), which provides a fine prediction of the segmentation regions, is applied to explicitly heighten the fine-detail information and reduce the dilution effect of boundary knowledge. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed PCPLP in boosting the learning ability to identify the lung infected regions with clear contours accurately. Our model is superior remarkably to the state-of-the-art segmentation models both quantitatively and qualitatively on a real CT dataset of COVID-19. äProgressive global perception and local polishing network for lung inf(epmc).pdf DeepDyve Pubget Overpricing