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10.1109/JBHI.2020.3023246 http://scihub22266oqcxt.onion/10.1109/JBHI.2020.3023246 32915751!8545175!32915751     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2020 ; 24 (10): 2806-2813 Nephropedia Template TP {{tp|p=32915751|t=2020. Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification |pdf=|usr=}}{{32915751}} gab.com Text Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=32915751 #FOAvip The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods. Twit Text FOAVip Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=32915751 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32915751 #FOAvip English Wikipedia <ref>{{Cite pmid|32915751}}</ref> Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification #MMPMID32915751 Wang Z; Liu Q; Dou Q IEEE J Biomed Health Inform 2020[Oct]; 24 (10): 2806-2813 PMID32915751show ga The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods. |*Betacoronavirus[MESH] |*Deep Learning[MESH] |*Pandemics/classification[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/*statistics & numerical data[MESH] |Computational Biology[MESH] |Computer Systems[MESH] |Coronavirus Infections/classification/*diagnosis/*diagnostic imaging[MESH] |Databases, Factual/statistics & numerical data[MESH] |Humans[MESH] |Machine Learning[MESH] |Pneumonia, Viral/classification/*diagnosis/*diagnostic imaging[MESH] |Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data[MESH] |SARS-CoV-2[MESH]Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT C(epmc).pdf DeepDyve Pubget Overpricing