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10.1109/TMI.2021.3066161 http://scihub22266oqcxt.onion/10.1109/TMI.2021.3066161 33760731!8544940!33760731     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Med+Imaging 2021 ; 40 (10): 2808-2819 Nephropedia Template TP {{tp|p=33760731|t=2021. Label-Free Segmentation of COVID-19 Lesions in Lung CT |pdf=|usr=}}{{33760731}} gab.com Text Label-Free Segmentation of COVID-19 Lesions in Lung CT JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33760731 #FOAvip Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via voxel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. Our modeling is inspired by the observation that the parts of tracheae and vessels, which lay in the high-intensity range where lesions belong to, exhibit strong patterns. To facilitate the learning of such patterns at a voxel level, we synthesize 'lesions' using a set of simple operations and insert the synthesized 'lesions' into normal CT lung scans to form training pairs, from which we learn a normalcy-recognizing network (NormNet) that recognizes normal tissues and separate them from possible COVID-19 lesions. Our experiments on three different public datasets validate the effectiveness of NormNet, which conspicuously outperforms a variety of unsupervised anomaly detection (UAD) methods. Twit Text FOAVip Label-Free Segmentation of COVID-19 Lesions in Lung CT ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33760731 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33760731 #FOAvip English Wikipedia <ref>{{Cite pmid|33760731}}</ref> Label-Free Segmentation of COVID-19 Lesions in Lung CT #MMPMID33760731 Yao Q; Xiao L; Liu P; Zhou SK IEEE Trans Med Imaging 2021[Oct]; 40 (10): 2808-2819 PMID33760731show ga Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via voxel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. Our modeling is inspired by the observation that the parts of tracheae and vessels, which lay in the high-intensity range where lesions belong to, exhibit strong patterns. To facilitate the learning of such patterns at a voxel level, we synthesize 'lesions' using a set of simple operations and insert the synthesized 'lesions' into normal CT lung scans to form training pairs, from which we learn a normalcy-recognizing network (NormNet) that recognizes normal tissues and separate them from possible COVID-19 lesions. Our experiments on three different public datasets validate the effectiveness of NormNet, which conspicuously outperforms a variety of unsupervised anomaly detection (UAD) methods. |*COVID-19[MESH] |COVID-19 Testing[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |SARS-CoV-2[MESH]Label-Free Segmentation of COVID-19 Lesions in Lung CT (epmc).pdf DeepDyve Pubget Overpricing