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10.1016/j.media.2021.102054 http://scihub22266oqcxt.onion/10.1016/j.media.2021.102054 33932751!8015379!33932751     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 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 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Med+Image+Anal 2021 ; 71 (ä): 102054 Nephropedia Template TP {{tp|p=33932751|t=2021. CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification |pdf=|usr=}}{{33932751}} gab.com Text CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification JO: Med Image Anal http://www.kidney.de/pget.php?&tw=1&pmid=33932751 #FOAvip The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87+/-0.01 vs. bacterial pneumonia, 0.93+/-0.01 vs. cancerous nodules, and 0.97+/-0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97+/-0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset. Twit Text FOAVip CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification ????Med Image Anal http://www.kidney.de/pget.php?&tw=1&pmid=33932751 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33932751 #FOAvip English Wikipedia <ref>{{Cite pmid|33932751}}</ref> CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification #MMPMID33932751 Goncharov M; Pisov M; Shevtsov A; Shirokikh B; Kurmukov A; Blokhin I; Chernina V; Solovev A; Gombolevskiy V; Morozov S; Belyaev M Med Image Anal 2021[Jul]; 71 (ä): 102054 PMID33932751show ga The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87+/-0.01 vs. bacterial pneumonia, 0.93+/-0.01 vs. cancerous nodules, and 0.97+/-0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97+/-0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset. |*COVID-19/diagnostic imaging[MESH] |*Deep Learning[MESH] |*Triage[MESH] |Humans[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH]CT-Based COVID-19 triage: Deep multitask learning improves joint ident(epmc).pdf DeepDyve Pubget Overpricing