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10.1016/j.ymeth.2021.07.001 http://scihub22266oqcxt.onion/10.1016/j.ymeth.2021.07.001 34237453!8256684!34237453     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       Methods 2022 ; 202 (ä): 62-69 Nephropedia Template TP {{tp|p=34237453|t=2022. COVID-19 lesion detection and segmentation-A deep learning method |pdf=|usr=}}{{34237453}} gab.com Text COVID-19 lesion detection and segmentation-A deep learning method JO: Methods http://www.kidney.de/pget.php?&tw=1&pmid=34237453 #FOAvip PURPOSE: In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and precise assistance for disease surveillance on the medical imaging aspect. MATERIALS AND METHODS: Basing on deep learning, we combined semantic segmentation and object detection methods to study the lesion performance of COVID-19. We put forward a novel end-to-end model which takes advantage of the Spatio-temporal features. Furthermore, a segmentation model attached with a fully connected CRF was designed for a more effective ROI input. RESULTS: Our method showed a better performance across different metrics against the comparison models. Moreover, our strategy highlighted strong robustness for the processed augmented testing samples. CONCLUSION: The comprehensive fusion of Spatio-temporal correlations can exploit more valuable features for locating target regions, and this mechanism is friendly to detect tiny lesions. Although it remains in discrete form, the feature extracting in temporal dimension improves the precision of final prediction. Twit Text FOAVip COVID-19 lesion detection and segmentation-A deep learning method ????Methods http://www.kidney.de/pget.php?&tw=1&pmid=34237453 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34237453 #FOAvip English Wikipedia <ref>{{Cite pmid|34237453}}</ref> COVID-19 lesion detection and segmentation-A deep learning method #MMPMID34237453 Jingxin L; Mengchao Z; Yuchen L; Jinglei C; Yutong Z; Zhong Z; Lihui Z Methods 2022[Jun]; 202 (ä): 62-69 PMID34237453show ga PURPOSE: In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and precise assistance for disease surveillance on the medical imaging aspect. MATERIALS AND METHODS: Basing on deep learning, we combined semantic segmentation and object detection methods to study the lesion performance of COVID-19. We put forward a novel end-to-end model which takes advantage of the Spatio-temporal features. Furthermore, a segmentation model attached with a fully connected CRF was designed for a more effective ROI input. RESULTS: Our method showed a better performance across different metrics against the comparison models. Moreover, our strategy highlighted strong robustness for the processed augmented testing samples. CONCLUSION: The comprehensive fusion of Spatio-temporal correlations can exploit more valuable features for locating target regions, and this mechanism is friendly to detect tiny lesions. Although it remains in discrete form, the feature extracting in temporal dimension improves the precision of final prediction. |*COVID-19/diagnostic imaging[MESH] |*Deep Learning[MESH] |Humans[MESH]COVID-19 lesion detection and segmentation-A deep learning method (epmc).pdf DeepDyve Pubget Overpricing