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10.1016/j.compbiomed.2020.104037 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2020.104037 33065387!7543793!33065387     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Biol+Med 2020 ; 126 (ä): 104037 Nephropedia Template TP {{tp|p=33065387|t=2020. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation |pdf=|usr=}}{{33065387}} gab.com Text Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33065387 #FOAvip This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification. Twit Text FOAVip Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33065387 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33065387 #FOAvip English Wikipedia <ref>{{Cite pmid|33065387}}</ref> Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation #MMPMID33065387 Amyar A; Modzelewski R; Li H; Ruan S Comput Biol Med 2020[Nov]; 126 (ä): 104037 PMID33065387show ga This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification. |*Betacoronavirus[MESH] |*Tomography, X-Ray Computed[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Deep Learning[MESH] |Female[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH]Multi-task deep learning based CT imaging analysis for COVID-19 pneumo(epmc).pdf DeepDyve Pubget Overpricing