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10.1016/j.bspc.2021.102901 http://scihub22266oqcxt.onion/10.1016/j.bspc.2021.102901 34178095!8220920!34178095     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Signal+Process+Control 2021 ; 69 (ä): 102901 Nephropedia Template TP {{tp|p=34178095|t=2021. Dense GAN and multi-layer attention based lesion segmentation method for COVID-19 CT images |pdf=|usr=}}{{34178095}} gab.com Text Dense GAN and multi-layer attention based lesion segmentation method for COVID-19 CT images JO: Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=34178095 #FOAvip As the COVID-19 virus spreads around the world, testing and screening of patients have become a headache for governments. With the accumulation of clinical diagnostic data, the imaging big data features of COVID-19 are gradually clear, and CT imaging diagnosis results become more important. To obtain clear lesion information from the CT images of patients' lungs is helpful for doctors to adopt effective medical methods, and at the same time, is helpful to screen the patients with real infection. Deep learning image segmentation is widely used in the field of medical image segmentation. However, there are some challenges in using deep learning to segment the lung lesions of COVID-19 patients. Since image segmentation requires the labeling of lesion information on a pixel by pixel basis, most professional radiologists need to screen and diagnose patients on the front line, and they do not have enough energy to label a large amount of image data. In this paper, an improved Dense GAN to expand data set is developed, and a multi-layer attention mechanism method, combined with U-Net's COVID-19 pulmonary CT image segmentation, is proposed. The experimental results showed that the segmentation method proposed in this paper improved the segmentation accuracy of COVID-19 pulmonary medical CT image by comparing with other image segmentation methods. Twit Text FOAVip Dense GAN and multi-layer attention based lesion segmentation method for COVID-19 CT images ????Biomed Signal Process Control http://www.kidney.de/pget.php?&tw=1&pmid=34178095 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34178095 #FOAvip English Wikipedia <ref>{{Cite pmid|34178095}}</ref> Dense GAN and multi-layer attention based lesion segmentation method for COVID-19 CT images #MMPMID34178095 Zhang J; Yu L; Chen D; Pan W; Shi C; Niu Y; Yao X; Xu X; Cheng Y Biomed Signal Process Control 2021[Aug]; 69 (ä): 102901 PMID34178095show ga As the COVID-19 virus spreads around the world, testing and screening of patients have become a headache for governments. With the accumulation of clinical diagnostic data, the imaging big data features of COVID-19 are gradually clear, and CT imaging diagnosis results become more important. To obtain clear lesion information from the CT images of patients' lungs is helpful for doctors to adopt effective medical methods, and at the same time, is helpful to screen the patients with real infection. Deep learning image segmentation is widely used in the field of medical image segmentation. However, there are some challenges in using deep learning to segment the lung lesions of COVID-19 patients. Since image segmentation requires the labeling of lesion information on a pixel by pixel basis, most professional radiologists need to screen and diagnose patients on the front line, and they do not have enough energy to label a large amount of image data. In this paper, an improved Dense GAN to expand data set is developed, and a multi-layer attention mechanism method, combined with U-Net's COVID-19 pulmonary CT image segmentation, is proposed. The experimental results showed that the segmentation method proposed in this paper improved the segmentation accuracy of COVID-19 pulmonary medical CT image by comparing with other image segmentation methods. äDense GAN and multi-layer attention based lesion segmentation method f(epmc).pdf DeepDyve Pubget Overpricing