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10.1109/TCBB.2021.3066331 http://scihub22266oqcxt.onion/10.1109/TCBB.2021.3066331 33729944!9647721!33729944     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/ACM+Trans+Comput+Biol+Bioinform 2021 ; PP (5): 2605-12 Nephropedia Template TP {{tp|p=33729944|t=2021. SODA: Detecting COVID-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation |pdf=|usr=}}{{33729944}} gab.com Text SODA: Detecting COVID-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation JO: IEEE/ACM Trans Comput Biol Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33729944 #FOAvip Due to the shortage of COVID-19 viral testing kits, radiology is used to complement the screening process. Deep learning methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance due to two issues, firstly the large domain shift present in chest x-ray datasets and secondly the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present results showing that SODA produces better pathology localizations. Twit Text FOAVip SODA: Detecting COVID-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation ????IEEE/ACM Trans Comput Biol Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33729944 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33729944 #FOAvip English Wikipedia <ref>{{Cite pmid|33729944}}</ref> SODA: Detecting COVID-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation #MMPMID33729944 Zhou J; Jing B; Wang Z; Xin H; Tong H IEEE/ACM Trans Comput Biol Bioinform 2021[Mar]; PP (5): 2605-12 PMID33729944show ga Due to the shortage of COVID-19 viral testing kits, radiology is used to complement the screening process. Deep learning methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance due to two issues, firstly the large domain shift present in chest x-ray datasets and secondly the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present results showing that SODA produces better pathology localizations. äSODA: Detecting COVID-19 in Chest X-rays with Semi-supervised Open Set(epmc).pdf DeepDyve Pubget Overpricing