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10.1109/JBHI.2021.3082527 http://scihub22266oqcxt.onion/10.1109/JBHI.2021.3082527 34033549!8545076!34033549     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2021 ; 25 (7): 2363-2373 Nephropedia Template TP {{tp|p=34033549|t=2021. Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19 |pdf=|usr=}}{{34033549}} gab.com Text Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19 JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=34033549 #FOAvip COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy. Twit Text FOAVip Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19 ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=34033549 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34033549 #FOAvip English Wikipedia <ref>{{Cite pmid|34033549}}</ref> Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19 #MMPMID34033549 Kitrungrotsakul T; Chen Q; Wu H; Iwamoto Y; Hu H; Zhu W; Chen C; Xu F; Zhou Y; Lin L; Tong R; Li J; Chen YW IEEE J Biomed Health Inform 2021[Jul]; 25 (7): 2363-2373 PMID34033549show ga COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy. |*Deep Learning[MESH] |COVID-19/*diagnostic imaging[MESH] |Databases, Factual[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH]Attention-RefNet: Interactive Attention Refinement Network for Infecte(epmc).pdf DeepDyve Pubget Overpricing