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10.1109/JBHI.2021.3103646 http://scihub22266oqcxt.onion/10.1109/JBHI.2021.3103646 34375293!8904133!34375293     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2021 ; 25 (11): 4140-4151 Nephropedia Template TP {{tp|p=34375293|t=2021. Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation |pdf=|usr=}}{{34375293}} gab.com Text Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=34375293 #FOAvip The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two models teach each other during training by using their respective predicted pseudo-labels of unlabeled data. Moreover, to alleviate the adverse impacts of noisy pseudo-labels for each model, we propose a self-ensembling strategy to perform consistency regularization for the up-to-date predictions of unlabeled data, in which the predictions of unlabeled data are gradually ensembled via moving average at the end of every training epoch. We evaluate our framework on a COVID-19 dataset containing 103 CT scans. Experimental results show that our proposed method achieves better performance in the case of only 4 labeled CT scans compared to the state-of-the-art semi-supervised segmentation networks. Twit Text FOAVip Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=34375293 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34375293 #FOAvip English Wikipedia <ref>{{Cite pmid|34375293}}</ref> Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation #MMPMID34375293 Li C; Dong L; Dou Q; Lin F; Zhang K; Feng Z; Si W; Deng X; Deng Z; Heng PA IEEE J Biomed Health Inform 2021[Nov]; 25 (11): 4140-4151 PMID34375293show ga The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two models teach each other during training by using their respective predicted pseudo-labels of unlabeled data. Moreover, to alleviate the adverse impacts of noisy pseudo-labels for each model, we propose a self-ensembling strategy to perform consistency regularization for the up-to-date predictions of unlabeled data, in which the predictions of unlabeled data are gradually ensembled via moving average at the end of every training epoch. We evaluate our framework on a COVID-19 dataset containing 103 CT scans. Experimental results show that our proposed method achieves better performance in the case of only 4 labeled CT scans compared to the state-of-the-art semi-supervised segmentation networks. |*COVID-19[MESH] |*Supervised Machine Learning[MESH] |Humans[MESH] |SARS-CoV-2[MESH]Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT (epmc).pdf DeepDyve Pubget Overpricing