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10.1016/j.asoc.2021.107330 http://scihub22266oqcxt.onion/10.1016/j.asoc.2021.107330 33776607!7979273!33776607     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       Appl+Soft+Comput 2021 ; 106 (ä): 107330 Nephropedia Template TP {{tp|p=33776607|t=2021. Federated learning for COVID-19 screening from Chest X-ray images |pdf=|usr=}}{{33776607}} gab.com Text Federated learning for COVID-19 screening from Chest X-ray images JO: Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33776607 #FOAvip Today, the whole world is facing a great medical disaster that affects the health and lives of the people: the COVID-19 disease, colloquially known as the Corona virus. Deep learning is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19. Such techniques involve large datasets for training and all such data must be centralized in order to be processed. Due to medical data privacy regulations, it is often not possible to collect and share patient data in a centralized data server. In this work, we present a collaborative federated learning framework allowing multiple medical institutions screening COVID-19 from Chest X-ray images using deep learning without sharing patient data. We investigate several key properties and specificities of federated learning setting including the not independent and identically distributed (non-IID) and unbalanced data distributions that naturally arise. We experimentally demonstrate that the proposed federated learning framework provides competitive results to that of models trained by sharing data, considering two different model architectures. These findings would encourage medical institutions to adopt collaborative process and reap benefits of the rich private data in order to rapidly build a powerful model for COVID-19 screening. Twit Text FOAVip Federated learning for COVID-19 screening from Chest X-ray images ????Appl Soft Comput http://www.kidney.de/pget.php?&tw=1&pmid=33776607 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33776607 #FOAvip English Wikipedia <ref>{{Cite pmid|33776607}}</ref> Federated learning for COVID-19 screening from Chest X-ray images #MMPMID33776607 Feki I; Ammar S; Kessentini Y; Muhammad K Appl Soft Comput 2021[Jul]; 106 (ä): 107330 PMID33776607show ga Today, the whole world is facing a great medical disaster that affects the health and lives of the people: the COVID-19 disease, colloquially known as the Corona virus. Deep learning is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19. Such techniques involve large datasets for training and all such data must be centralized in order to be processed. Due to medical data privacy regulations, it is often not possible to collect and share patient data in a centralized data server. In this work, we present a collaborative federated learning framework allowing multiple medical institutions screening COVID-19 from Chest X-ray images using deep learning without sharing patient data. We investigate several key properties and specificities of federated learning setting including the not independent and identically distributed (non-IID) and unbalanced data distributions that naturally arise. We experimentally demonstrate that the proposed federated learning framework provides competitive results to that of models trained by sharing data, considering two different model architectures. These findings would encourage medical institutions to adopt collaborative process and reap benefits of the rich private data in order to rapidly build a powerful model for COVID-19 screening. äFederated learning for COVID-19 screening from Chest X-ray images (epmc).pdf DeepDyve Pubget Overpricing