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10.1109/TMI.2020.2994908 http://scihub22266oqcxt.onion/10.1109/TMI.2020.2994908 32730210!ä!32730210 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 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 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+Trans+Med+Imaging 2020 ; 39 (8): 2572-2583 Nephropedia Template TP {{tp|p=32730210|t=2020. Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images |pdf=|usr=}}{{32730210}} gab.com Text Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32730210 #FOAvip We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Interstitial Lung Disease (ILD) caused by other viruses. In the proposed method, two 3D-ResNets are coupled together into a single model for the two above-mentioned tasks via a novel prior-attention strategy. We extend residual learning with the proposed prior-attention mechanism and design a new so-called prior-attention residual learning (PARL) block. The model can be easily built by stacking the PARL blocks and trained end-to-end using multi-task losses. More specifically, one 3D-ResNet branch is trained as a binary classifier using lung images with and without pneumonia so that it can highlight the lesion areas within the lungs. Simultaneously, inside the PARL blocks, prior-attention maps are generated from this branch and used to guide another branch to learn more discriminative representations for the pneumonia-type classification. Experimental results demonstrate that the proposed framework can significantly improve the performance of COVID-19 screening. Compared to other methods, it achieves a state-of-the-art result. Moreover, the proposed method can be easily extended to other similar clinical applications such as computer-aided detection and diagnosis of pulmonary nodules in CT images, glaucoma lesions in Retina fundus images, etc. Twit Text FOAVip Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32730210 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32730210 #FOAvip English Wikipedia <ref>{{Cite pmid|32730210}}</ref> Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images #MMPMID32730210 Wang J; Bao Y; Wen Y; Lu H; Luo H; Xiang Y; Li X; Liu C; Qian D IEEE Trans Med Imaging 2020[Aug]; 39 (8): 2572-2583 PMID32730210show ga We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Interstitial Lung Disease (ILD) caused by other viruses. In the proposed method, two 3D-ResNets are coupled together into a single model for the two above-mentioned tasks via a novel prior-attention strategy. We extend residual learning with the proposed prior-attention mechanism and design a new so-called prior-attention residual learning (PARL) block. The model can be easily built by stacking the PARL blocks and trained end-to-end using multi-task losses. More specifically, one 3D-ResNet branch is trained as a binary classifier using lung images with and without pneumonia so that it can highlight the lesion areas within the lungs. Simultaneously, inside the PARL blocks, prior-attention maps are generated from this branch and used to guide another branch to learn more discriminative representations for the pneumonia-type classification. Experimental results demonstrate that the proposed framework can significantly improve the performance of COVID-19 screening. Compared to other methods, it achieves a state-of-the-art result. Moreover, the proposed method can be easily extended to other similar clinical applications such as computer-aided detection and diagnosis of pulmonary nodules in CT images, glaucoma lesions in Retina fundus images, etc. |*Deep Learning[MESH] |Adult[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Humans[MESH] |Imaging, Three-Dimensional[MESH] |Lung/diagnostic imaging[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Radiography, Thoracic[MESH] |SARS-CoV-2[MESH]Prior-Attention Residual Learning for More Discriminative COVID-19 Scr(epmc).pdf DeepDyve Pubget Overpricing