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10.1016/j.ins.2021.07.059 http://scihub22266oqcxt.onion/10.1016/j.ins.2021.07.059 34305162!8294559!34305162     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34305162&cmd=llinks): Failed to open stream: HTTP request failed! 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RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions |pdf=|usr=}}{{34305162}} gab.com Text RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions JO: Inf Sci (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=34305162 #FOAvip The segmentation of COVID-19 lesions from computed tomography (CT) scans is crucial to develop an efficient automated diagnosis system. Deep learning (DL) has shown success in different segmentation tasks. However, an efficient DL approach requires a large amount of accurately annotated data, which is difficult to aggregate owing to the urgent situation of COVID-19. Inaccurate annotation can easily occur without experts, and segmentation performance is substantially worsened by noisy annotations. Therefore, this study presents a reliable and consistent temporal-ensembling (RCTE) framework for semi-supervised lesion segmentation. A segmentation network is integrated into a teacher-student architecture to segment infection regions from a limited number of annotated CT scans and a large number of unannotated CT scans. The network generates reliable and unreliable targets, and to evenly handle these targets potentially degrades performance. To address this, a reliable teacher-student architecture is introduced, where a reliable teacher network is the exponential moving average (EMA) of a reliable student network that is reliably renovated by restraining the student involvement to EMA when its loss is larger. We also present a noise-aware loss based on improvements to generalized cross-entropy loss to lead the segmentation performance toward noisy annotations. Comprehensive analysis validates the robustness of RCTE over recent cutting-edge semi-supervised segmentation techniques, with a 65.87% Dice score. Twit Text FOAVip RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions ????Inf Sci (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=34305162 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34305162 #FOAvip English Wikipedia <ref>{{Cite pmid|34305162}}</ref> RCTE: A reliable and consistent temporal-ensembling framework for semi-supervised segmentation of COVID-19 lesions #MMPMID34305162 Ding W; Abdel-Basset M; Hawash H Inf Sci (N Y) 2021[Nov]; 578 (ä): 559-573 PMID34305162show ga The segmentation of COVID-19 lesions from computed tomography (CT) scans is crucial to develop an efficient automated diagnosis system. Deep learning (DL) has shown success in different segmentation tasks. However, an efficient DL approach requires a large amount of accurately annotated data, which is difficult to aggregate owing to the urgent situation of COVID-19. Inaccurate annotation can easily occur without experts, and segmentation performance is substantially worsened by noisy annotations. Therefore, this study presents a reliable and consistent temporal-ensembling (RCTE) framework for semi-supervised lesion segmentation. A segmentation network is integrated into a teacher-student architecture to segment infection regions from a limited number of annotated CT scans and a large number of unannotated CT scans. The network generates reliable and unreliable targets, and to evenly handle these targets potentially degrades performance. To address this, a reliable teacher-student architecture is introduced, where a reliable teacher network is the exponential moving average (EMA) of a reliable student network that is reliably renovated by restraining the student involvement to EMA when its loss is larger. We also present a noise-aware loss based on improvements to generalized cross-entropy loss to lead the segmentation performance toward noisy annotations. Comprehensive analysis validates the robustness of RCTE over recent cutting-edge semi-supervised segmentation techniques, with a 65.87% Dice score. äRCTE: A reliable and consistent temporal-ensembling framework for semi(epmc).pdf DeepDyve Pubget Overpricing