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10.1109/TMI.2021.3104474 http://scihub22266oqcxt.onion/10.1109/TMI.2021.3104474 34383647!8905616!34383647     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Med+Imaging 2022 ; 41 (1): 88-102 Nephropedia Template TP {{tp|p=34383647|t=2022. Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation |pdf=|usr=}}{{34383647}} gab.com Text Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=34383647 #FOAvip Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches. Twit Text FOAVip Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=34383647 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34383647 #FOAvip English Wikipedia <ref>{{Cite pmid|34383647}}</ref> Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation #MMPMID34383647 Xu GX; Liu C; Liu J; Ding Z; Shi F; Guo M; Zhao W; Li X; Wei Y; Gao Y; Ren CX; Shen D IEEE Trans Med Imaging 2022[Jan]; 41 (1): 88-102 PMID34383647show ga Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches. |*COVID-19[MESH] |Humans[MESH] |SARS-CoV-2[MESH]Cross-Site Severity Assessment of COVID-19 From CT Images via Domain A(epmc).pdf DeepDyve Pubget Overpricing