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10.3233/SHTI210223 http://scihub22266oqcxt.onion/10.3233/SHTI210223 34042629!ä!34042629 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       Stud+Health+Technol+Inform 2021 ; 281 (ä): 518-519 Nephropedia Template TP {{tp|p=34042629|t=2021. COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning |pdf=|usr=}}{{34042629}} gab.com Text COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning JO: Stud Health Technol Inform http://www.kidney.de/pget.php?&tw=1&pmid=34042629 #FOAvip Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 infection in CT scans using deep convolutional neural networks. Furthermore, we evaluate the performance impact of ensemble learning techniques (Bagging and Augmenting). Our models showed highly accurate segmentation results, in which Bagging achieved the highest dice similarity coefficient. Twit Text FOAVip COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning ????Stud Health Technol Inform http://www.kidney.de/pget.php?&tw=1&pmid=34042629 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34042629 #FOAvip English Wikipedia <ref>{{Cite pmid|34042629}}</ref> COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning #MMPMID34042629 Meyer P; Muller D; Soto-Rey I; Kramer F Stud Health Technol Inform 2021[May]; 281 (ä): 518-519 PMID34042629show ga Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 infection in CT scans using deep convolutional neural networks. Furthermore, we evaluate the performance impact of ensemble learning techniques (Bagging and Augmenting). Our models showed highly accurate segmentation results, in which Bagging achieved the highest dice similarity coefficient. |*COVID-19[MESH] |*Deep Learning[MESH] |COVID-19 Testing[MESH] |Humans[MESH] |Image Processing, Computer-Assisted[MESH] |Neural Networks, Computer[MESH]COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learni(epmc).pdf DeepDyve Pubget Overpricing