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10.1016/j.patcog.2021.108005 http://scihub22266oqcxt.onion/10.1016/j.patcog.2021.108005 33972808!8099585!33972808     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Recognit 2021 ; 118 (ä): 108005 Nephropedia Template TP {{tp|p=33972808|t=2021. Periphery-aware COVID-19 diagnosis with contrastive representation enhancement |pdf=|usr=}}{{33972808}} gab.com Text Periphery-aware COVID-19 diagnosis with contrastive representation enhancement JO: Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=33972808 #FOAvip Computer-aided diagnosis has been extensively investigated for more rapid and accurate screening during the outbreak of COVID-19 epidemic. However, the challenge remains to distinguish COVID-19 in the complex scenario of multi-type pneumonia classification and improve the overall diagnostic performance. In this paper, we propose a novel periphery-aware COVID-19 diagnosis approach with contrastive representation enhancement to identify COVID-19 from influenza-A (H1N1) viral pneumonia, community acquired pneumonia (CAP), and healthy subjects using chest CT images. Our key contributions include: 1) an unsupervised Periphery-aware Spatial Prediction (PSP) task which is designed to introduce important spatial patterns into deep networks; 2) an adaptive Contrastive Representation Enhancement (CRE) mechanism which can effectively capture the intra-class similarity and inter-class difference of various types of pneumonia. We integrate PSP and CRE to obtain the representations which are highly discriminative in COVID-19 screening. We evaluate our approach comprehensively on our constructed large-scale dataset and two public datasets. Extensive experiments on both volume-level and slice-level CT images demonstrate the effectiveness of our proposed approach with PSP and CRE for COVID-19 diagnosis. Twit Text FOAVip Periphery-aware COVID-19 diagnosis with contrastive representation enhancement ????Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=33972808 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33972808 #FOAvip English Wikipedia <ref>{{Cite pmid|33972808}}</ref> Periphery-aware COVID-19 diagnosis with contrastive representation enhancement #MMPMID33972808 Hou J; Xu J; Jiang L; Du S; Feng R; Zhang Y; Shan F; Xue X Pattern Recognit 2021[Oct]; 118 (ä): 108005 PMID33972808show ga Computer-aided diagnosis has been extensively investigated for more rapid and accurate screening during the outbreak of COVID-19 epidemic. However, the challenge remains to distinguish COVID-19 in the complex scenario of multi-type pneumonia classification and improve the overall diagnostic performance. In this paper, we propose a novel periphery-aware COVID-19 diagnosis approach with contrastive representation enhancement to identify COVID-19 from influenza-A (H1N1) viral pneumonia, community acquired pneumonia (CAP), and healthy subjects using chest CT images. Our key contributions include: 1) an unsupervised Periphery-aware Spatial Prediction (PSP) task which is designed to introduce important spatial patterns into deep networks; 2) an adaptive Contrastive Representation Enhancement (CRE) mechanism which can effectively capture the intra-class similarity and inter-class difference of various types of pneumonia. We integrate PSP and CRE to obtain the representations which are highly discriminative in COVID-19 screening. We evaluate our approach comprehensively on our constructed large-scale dataset and two public datasets. Extensive experiments on both volume-level and slice-level CT images demonstrate the effectiveness of our proposed approach with PSP and CRE for COVID-19 diagnosis. äPeriphery-aware COVID-19 diagnosis with contrastive representation enh(epmc).pdf DeepDyve Pubget Overpricing