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10.1109/JBHI.2020.3018181 http://scihub22266oqcxt.onion/10.1109/JBHI.2020.3018181 32816680!8545166!32816680     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+J+Biomed+Health+Inform 2020 ; 24 (10): 2787-2797 Nephropedia Template TP {{tp|p=32816680|t=2020. Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank |pdf=|usr=}}{{32816680}} gab.com Text Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=32816680 #FOAvip Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels ('difficulty' and 'diversity') are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data 'values' of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time. Twit Text FOAVip Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=32816680 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32816680 #FOAvip English Wikipedia <ref>{{Cite pmid|32816680}}</ref> Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank #MMPMID32816680 Li Y; Wei D; Chen J; Cao S; Zhou H; Zhu Y; Wu J; Lan L; Sun W; Qian T; Ma K; Xu H; Zheng Y IEEE J Biomed Health Inform 2020[Oct]; 24 (10): 2787-2797 PMID32816680show ga Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels ('difficulty' and 'diversity') are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data 'values' of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time. |*Betacoronavirus[MESH] |*Pandemics/classification[MESH] |*Supervised Machine Learning[MESH] |Algorithms[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/*statistics & numerical data[MESH] |Cohort Studies[MESH] |Computational Biology[MESH] |Coronavirus Infections/classification/*diagnosis/*diagnostic imaging[MESH] |Deep Learning[MESH] |Diagnostic Errors/statistics & numerical data[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |Pneumonia, Viral/classification/*diagnosis/*diagnostic imaging[MESH] |Radiographic Image Interpretation, Computer-Assisted/*statistics & numerical data[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]Efficient and Effective Training of COVID-19 Classification Networks W(epmc).pdf DeepDyve Pubget Overpricing