Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1109/JBHI.2021.3104629 http://scihub22266oqcxt.onion/10.1109/JBHI.2021.3104629 34388102!8843059!34388102     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2021 ; 25 (11): 4119-4127 Nephropedia Template TP {{tp|p=34388102|t=2021. COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors |pdf=|usr=}}{{34388102}} gab.com Text COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=34388102 #FOAvip Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ray (CXR) is a promising method for early diagnoses of pulmonary diseases. Recently, deep learning has achieved great success in supervised lung segmentation. However, how to effectively utilize the lung region in screening COVID-19 still remains a challenge due to domain shift and lack of manual pixel-level annotations. We hereby propose a multi-appearance COVID-19 screening framework by using lung region priors derived from CXR images. Firstly, we propose a multi-scale adversarial domain adaptation network (MS-AdaNet) to boost the cross-domain lung segmentation task as the prior knowledge to the classification network. Then, we construct a multi-appearance network (MA-Net), which is composed of three sub-networks to realize multi-appearance feature extraction and fusion using lung region priors. At last, we can obtain prediction results from normal, viral pneumonia, and COVID-19 using the proposed MA-Net. We extend the proposed MS-AdaNet for lung segmentation task on three different public CXR datasets. The results suggest that the MS-AdaNet outperforms contrastive methods in cross-domain lung segmentation. Moreover, experiments reveal that the proposed MA-Net achieves accuracy of 98.83 % and F1-score of 98.71 % on COVID-19 screening. The results indicate that the proposed MA-Net can obtain significant performance on COVID-19 screening. Twit Text FOAVip COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=34388102 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34388102 #FOAvip English Wikipedia <ref>{{Cite pmid|34388102}}</ref> COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors #MMPMID34388102 An J; Cai Q; Qu Z; Gao Z IEEE J Biomed Health Inform 2021[Nov]; 25 (11): 4119-4127 PMID34388102show ga Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ray (CXR) is a promising method for early diagnoses of pulmonary diseases. Recently, deep learning has achieved great success in supervised lung segmentation. However, how to effectively utilize the lung region in screening COVID-19 still remains a challenge due to domain shift and lack of manual pixel-level annotations. We hereby propose a multi-appearance COVID-19 screening framework by using lung region priors derived from CXR images. Firstly, we propose a multi-scale adversarial domain adaptation network (MS-AdaNet) to boost the cross-domain lung segmentation task as the prior knowledge to the classification network. Then, we construct a multi-appearance network (MA-Net), which is composed of three sub-networks to realize multi-appearance feature extraction and fusion using lung region priors. At last, we can obtain prediction results from normal, viral pneumonia, and COVID-19 using the proposed MA-Net. We extend the proposed MS-AdaNet for lung segmentation task on three different public CXR datasets. The results suggest that the MS-AdaNet outperforms contrastive methods in cross-domain lung segmentation. Moreover, experiments reveal that the proposed MA-Net achieves accuracy of 98.83 % and F1-score of 98.71 % on COVID-19 screening. The results indicate that the proposed MA-Net can obtain significant performance on COVID-19 screening. |*COVID-19[MESH] |*Deep Learning[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |SARS-CoV-2[MESH]COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors (epmc).pdf DeepDyve Pubget Overpricing