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10.1016/j.artmed.2021.102082 http://scihub22266oqcxt.onion/10.1016/j.artmed.2021.102082 34127245!8153959!34127245     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Artif+Intell+Med 2021 ; 117 (ä): 102082 Nephropedia Template TP {{tp|p=34127245|t=2021. NIA-Network: Towards improving lung CT infection detection for COVID-19 diagnosis |pdf=|usr=}}{{34127245}} gab.com Text NIA-Network: Towards improving lung CT infection detection for COVID-19 diagnosis JO: Artif Intell Med http://www.kidney.de/pget.php?&tw=1&pmid=34127245 #FOAvip During pandemics (e.g., COVID-19) physicians have to focus on diagnosing and treating patients, which often results in that only a limited amount of labeled CT images is available. Although recent semi-supervised learning algorithms may alleviate the problem of annotation scarcity, limited real-world CT images still cause those algorithms producing inaccurate detection results, especially in real-world COVID-19 cases. Existing models often cannot detect the small infected regions in COVID-19 CT images, such a challenge implicitly causes that many patients with minor symptoms are misdiagnosed and develop more severe symptoms, causing a higher mortality. In this paper, we propose a new method to address this challenge. Not only can we detect severe cases, but also detect minor symptoms using real-world COVID-19 CT images in which the source domain only includes limited labeled CT images but the target domain has a lot of unlabeled CT images. Specifically, we adopt Network-in-Network and Instance Normalization to build a new module (we term it NI module) and extract discriminative representations from CT images from both source and target domains. A domain classifier is utilized to implement infected region adaptation from source domain to target domain in an Adversarial Learning manner, and learns domain-invariant region proposal network (RPN) in the Faster R-CNN model. We call our model NIA-Network (Network-in-Network, Instance Normalization and Adversarial Learning), and conduct extensive experiments on two COVID-19 datasets to validate our approach. The experimental results show that our model can effectively detect infected regions with different sizes and achieve the highest diagnostic accuracy compared with existing SOTA methods. Twit Text FOAVip NIA-Network: Towards improving lung CT infection detection for COVID-19 diagnosis ????Artif Intell Med http://www.kidney.de/pget.php?&tw=1&pmid=34127245 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34127245 #FOAvip English Wikipedia <ref>{{Cite pmid|34127245}}</ref> NIA-Network: Towards improving lung CT infection detection for COVID-19 diagnosis #MMPMID34127245 Li W; Chen J; Chen P; Yu L; Cui X; Li Y; Cheng F; Ouyang W Artif Intell Med 2021[Jul]; 117 (ä): 102082 PMID34127245show ga During pandemics (e.g., COVID-19) physicians have to focus on diagnosing and treating patients, which often results in that only a limited amount of labeled CT images is available. Although recent semi-supervised learning algorithms may alleviate the problem of annotation scarcity, limited real-world CT images still cause those algorithms producing inaccurate detection results, especially in real-world COVID-19 cases. Existing models often cannot detect the small infected regions in COVID-19 CT images, such a challenge implicitly causes that many patients with minor symptoms are misdiagnosed and develop more severe symptoms, causing a higher mortality. In this paper, we propose a new method to address this challenge. Not only can we detect severe cases, but also detect minor symptoms using real-world COVID-19 CT images in which the source domain only includes limited labeled CT images but the target domain has a lot of unlabeled CT images. Specifically, we adopt Network-in-Network and Instance Normalization to build a new module (we term it NI module) and extract discriminative representations from CT images from both source and target domains. A domain classifier is utilized to implement infected region adaptation from source domain to target domain in an Adversarial Learning manner, and learns domain-invariant region proposal network (RPN) in the Faster R-CNN model. We call our model NIA-Network (Network-in-Network, Instance Normalization and Adversarial Learning), and conduct extensive experiments on two COVID-19 datasets to validate our approach. The experimental results show that our model can effectively detect infected regions with different sizes and achieve the highest diagnostic accuracy compared with existing SOTA methods. |*COVID-19/diagnosis[MESH] |*Lung/diagnostic imaging[MESH] |*Machine Learning[MESH] |Algorithms[MESH] |COVID-19 Testing[MESH] |Humans[MESH]NIA-Network: Towards improving lung CT infection detection for COVID-1(epmc).pdf DeepDyve Pubget Overpricing