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10.1016/j.media.2021.101978 http://scihub22266oqcxt.onion/10.1016/j.media.2021.101978 33588121!7857016!33588121     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33588121&cmd=llinks): Failed to open stream: HTTP request failed! 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A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning |pdf=|usr=}}{{33588121}} gab.com Text A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning JO: Med Image Anal http://www.kidney.de/pget.php?&tw=1&pmid=33588121 #FOAvip How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. Twit Text FOAVip A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning ????Med Image Anal http://www.kidney.de/pget.php?&tw=1&pmid=33588121 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33588121 #FOAvip English Wikipedia <ref>{{Cite pmid|33588121}}</ref> A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning #MMPMID33588121 Li Z; Zhao W; Shi F; Qi L; Xie X; Wei Y; Ding Z; Gao Y; Wu S; Liu J; Shi Y; Shen D Med Image Anal 2021[Apr]; 69 (ä): 101978 PMID33588121show ga How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/*diagnostic imaging[MESH] |Child[MESH] |Child, Preschool[MESH] |Deep Learning[MESH] |Female[MESH] |Humans[MESH] |Infant[MESH] |Infant, Newborn[MESH] |Male[MESH] |Middle Aged[MESH] |SARS-CoV-2[MESH] |Severity of Illness Index[MESH] |Supervised Machine Learning[MESH] |Tomography, X-Ray Computed[MESH]A novel multiple instance learning framework for COVID-19 severity ass(epmc).pdf DeepDyve Pubget Overpricing