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10.1088/2057-1976/ac008a http://scihub22266oqcxt.onion/10.1088/2057-1976/ac008a 33979791!ä!33979791 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Phys+Eng+Express 2021 ; 7 (4): ä Nephropedia Template TP {{tp|p=33979791|t=2021. Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet |pdf=|usr=}}{{33979791}} gab.com Text Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet JO: Biomed Phys Eng Express http://www.kidney.de/pget.php?&tw=1&pmid=33979791 #FOAvip Segmenting lesion regions of Coronavirus Disease 2019 (COVID-19) from computed tomography (CT) images is a challenge owing to COVID-19 lesions characterized by high variation, low contrast between infection lesions and around normal tissues, and blurred boundaries of infections. Moreover, a shortage of available CT dataset hinders deep learning techniques applying to tackling COVID-19. To address these issues, we propose a deep learning-based approach known as PPM-Unet to segmenting COVID-19 lesions from CT images. Our method improves an Unet by adopting pyramid pooling modules instead of the conventional skip connection and then enhances the representation of the neural network by aiding the global attention mechanism. We first pre-train PPM-Unet on COVID-19 dataset of pseudo labels containing1600 samples producing a coarse model. Then we fine-tune the coarse PPM-Unet on the standard COVID-19 dataset consisting of 100 pairs of samples to achieve a fine PPM-Unet. Qualitative and quantitative results demonstrate that our method can accurately segment COVID-19 infection regions from CT images, and achieve higher performance than other state-of-the-art segmentation models in this study. It offers a promising tool to lay a foundation for quantitatively detecting COVID-19 lesions. Twit Text FOAVip Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet ????Biomed Phys Eng Express http://www.kidney.de/pget.php?&tw=1&pmid=33979791 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33979791 #FOAvip English Wikipedia <ref>{{Cite pmid|33979791}}</ref> Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet #MMPMID33979791 Ma Y; Feng P; He P; Ren Y; Guo X; Yu X; Wei B Biomed Phys Eng Express 2021[May]; 7 (4): ä PMID33979791show ga Segmenting lesion regions of Coronavirus Disease 2019 (COVID-19) from computed tomography (CT) images is a challenge owing to COVID-19 lesions characterized by high variation, low contrast between infection lesions and around normal tissues, and blurred boundaries of infections. Moreover, a shortage of available CT dataset hinders deep learning techniques applying to tackling COVID-19. To address these issues, we propose a deep learning-based approach known as PPM-Unet to segmenting COVID-19 lesions from CT images. Our method improves an Unet by adopting pyramid pooling modules instead of the conventional skip connection and then enhances the representation of the neural network by aiding the global attention mechanism. We first pre-train PPM-Unet on COVID-19 dataset of pseudo labels containing1600 samples producing a coarse model. Then we fine-tune the coarse PPM-Unet on the standard COVID-19 dataset consisting of 100 pairs of samples to achieve a fine PPM-Unet. Qualitative and quantitative results demonstrate that our method can accurately segment COVID-19 infection regions from CT images, and achieve higher performance than other state-of-the-art segmentation models in this study. It offers a promising tool to lay a foundation for quantitatively detecting COVID-19 lesions. |*Deep Learning[MESH] |*Neural Networks, Computer[MESH] |Algorithms[MESH] |COVID-19/*complications/virology[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/*methods[MESH] |Lung Diseases/diagnostic imaging/*pathology/virology[MESH] |SARS-CoV-2/*isolation & purification[MESH] |Specimen Handling[MESH]Segmenting lung lesions of COVID-19 from CT images via pyramid pooling(epmc).pdf DeepDyve Pubget Overpricing