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10.1109/TMI.2020.2995508 http://scihub22266oqcxt.onion/10.1109/TMI.2020.2995508 32730212!ä!32730212 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       IEEE+Trans+Med+Imaging 2020 ; 39 (8): 2595-2605 Nephropedia Template TP {{tp|p=32730212|t=2020. Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia |pdf=|usr=}}{{32730212}} gab.com Text Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32730212 #FOAvip The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak. Twit Text FOAVip Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32730212 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32730212 #FOAvip English Wikipedia <ref>{{Cite pmid|32730212}}</ref> Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia #MMPMID32730212 Ouyang X; Huo J; Xia L; Shan F; Liu J; Mo Z; Yan F; Ding Z; Yang Q; Song B; Shi F; Yuan H; Wei Y; Cao X; Gao Y; Wu D; Wang Q; Shen D IEEE Trans Med Imaging 2020[Aug]; 39 (8): 2595-2605 PMID32730212show ga The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak. |*Deep Learning[MESH] |Algorithms[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Community-Acquired Infections/diagnostic imaging[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |ROC Curve[MESH] |Radiography, Thoracic[MESH] |SARS-CoV-2[MESH]Dual-Sampling Attention Network for Diagnosis of COVID-19 From Communi(epmc).pdf DeepDyve Pubget Overpricing