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10.1109/TMI.2020.3000314 http://scihub22266oqcxt.onion/10.1109/TMI.2020.3000314 32730215!8544954!32730215     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 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       IEEE+Trans+Med+Imaging 2020 ; 39 (8): 2653-2663 Nephropedia Template TP {{tp|p=32730215|t=2020. A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images |pdf=|usr=}}{{32730215}} gab.com Text A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32730215 #FOAvip Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation. Twit Text FOAVip A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32730215 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32730215 #FOAvip English Wikipedia <ref>{{Cite pmid|32730215}}</ref> A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images #MMPMID32730215 Wang G; Liu X; Li C; Xu Z; Ruan J; Zhu H; Meng T; Li K; Huang N; Zhang S IEEE Trans Med Imaging 2020[Aug]; 39 (8): 2653-2663 PMID32730215show ga Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation. |*Deep Learning[MESH] |Algorithms[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |SARS-CoV-2[MESH]A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumo(epmc).pdf DeepDyve Pubget Overpricing