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10.3390/s21062215 http://scihub22266oqcxt.onion/10.3390/s21062215 33810066!8004971!33810066     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sensors+(Basel) 2021 ; 21 (6): ä Nephropedia Template TP {{tp|p=33810066|t=2021. A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images |pdf=|usr=}}{{33810066}} gab.com Text A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33810066 #FOAvip Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net. Experimental results indicate improvement in the segmentation accuracy of identifying COVID-19 infected regions. In particular, using 4-fold cross-validation results of the different classifiers, we observed an improvement of 5.388 +/- 3.046% for all test data regarding the IoU metric and a similar increment of 5.394 +/- 3.015% for the F1 score. Moreover, the statistical significance of the improvement obtained using our proposed few-shot U-Net architecture compared with the traditional U-Net model was confirmed by applying the Kruskal-Wallis test (p-value = 0.026). Twit Text FOAVip A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33810066 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33810066 #FOAvip English Wikipedia <ref>{{Cite pmid|33810066}}</ref> A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images #MMPMID33810066 Voulodimos A; Protopapadakis E; Katsamenis I; Doulamis A; Doulamis N Sensors (Basel) 2021[Mar]; 21 (6): ä PMID33810066show ga Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net. Experimental results indicate improvement in the segmentation accuracy of identifying COVID-19 infected regions. In particular, using 4-fold cross-validation results of the different classifiers, we observed an improvement of 5.388 +/- 3.046% for all test data regarding the IoU metric and a similar increment of 5.394 +/- 3.015% for the F1 score. Moreover, the statistical significance of the improvement obtained using our proposed few-shot U-Net architecture compared with the traditional U-Net model was confirmed by applying the Kruskal-Wallis test (p-value = 0.026). |*Deep Learning[MESH] |*Tomography, X-Ray Computed[MESH] |COVID-19/*diagnostic imaging[MESH]A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmen(epmc).pdf DeepDyve Pubget Overpricing