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10.1109/JBHI.2021.3058293 http://scihub22266oqcxt.onion/10.1109/JBHI.2021.3058293 33560995!8545167!33560995     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 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 (5): 1336-1346 Nephropedia Template TP {{tp|p=33560995|t=2021. Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images |pdf=|usr=}}{{33560995}} gab.com Text Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33560995 #FOAvip Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD. Twit Text FOAVip Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=33560995 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33560995 #FOAvip English Wikipedia <ref>{{Cite pmid|33560995}}</ref> Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images #MMPMID33560995 Li J; Wang Y; Wang S; Wang J; Liu J; Jin Q; Sun L IEEE J Biomed Health Inform 2021[May]; 25 (5): 1336-1346 PMID33560995show ga Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD. |*Deep Learning[MESH] |COVID-19/*diagnostic imaging[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |Radiography, Thoracic/*methods[MESH]Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest(epmc).pdf DeepDyve Pubget Overpricing