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10.1109/TMI.2020.2994459 http://scihub22266oqcxt.onion/10.1109/TMI.2020.2994459 32406829!ä!32406829 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Med+Imaging 2020 ; 39 (8): 2676-2687 Nephropedia Template TP {{tp|p=32406829|t=2020. Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound |pdf=|usr=}}{{32406829}} gab.com Text Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32406829 #FOAvip Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data. Twit Text FOAVip Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=32406829 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32406829 #FOAvip English Wikipedia <ref>{{Cite pmid|32406829}}</ref> Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound #MMPMID32406829 Roy S; Menapace W; Oei S; Luijten B; Fini E; Saltori C; Huijben I; Chennakeshava N; Mento F; Sentelli A; Peschiera E; Trevisan R; Maschietto G; Torri E; Inchingolo R; Smargiassi A; Soldati G; Rota P; Passerini A; van Sloun RJG; Ricci E; Demi L IEEE Trans Med Imaging 2020[Aug]; 39 (8): 2676-2687 PMID32406829show ga Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data. |*Deep Learning[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted/*methods[MESH] |Lung/diagnostic imaging[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Point-of-Care Systems[MESH] |SARS-CoV-2[MESH]Deep Learning for Classification and Localization of COVID-19 Markers (epmc).pdf DeepDyve Pubget Overpricing