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10.3390/ijerph17186933 http://scihub22266oqcxt.onion/10.3390/ijerph17186933 32971995!7557723!32971995     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 17 (18): ä Nephropedia Template TP {{tp|p=32971995|t=2020. Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data |pdf=|usr=}}{{32971995}} gab.com Text Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32971995 #FOAvip The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR. Twit Text FOAVip Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32971995 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32971995 #FOAvip English Wikipedia <ref>{{Cite pmid|32971995}}</ref> Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data #MMPMID32971995 Tartaglione E; Barbano CA; Berzovini C; Calandri M; Grangetto M Int J Environ Res Public Health 2020[Sep]; 17 (18): ä PMID32971995show ga The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR. |*Deep Learning[MESH] |*Radiography, Thoracic[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging/epidemiology[MESH] |Datasets as Topic[MESH] |Humans[MESH] |Italy/epidemiology[MESH] |Pandemics[MESH]Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race(epmc).pdf DeepDyve Pubget Overpricing