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10.3348/kjr.2017.18.4.570 http://scihub22266oqcxt.onion/10.3348/kjr.2017.18.4.570 C5447633!5447633 !28670152     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28670152 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Korean+J+Radiol 2017 ; 18 (4 ): 570-584 Nephropedia Template TP {{tp|p=28670152 |t=2017. Deep Learning in Medical Imaging: General Overview |pdf=|usr=}}{{28670152 }} gab.com Text Deep Learning in Medical Imaging: General Overview JO: Korean J Radiol http://www.kidney.de/pget.php?&tw=1&pmid=28670152 #FOAvip The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. Twit Text FOAVip Deep Learning in Medical Imaging: General Overview ????Korean J Radiol http://www.kidney.de/pget.php?&tw=1&pmid=28670152 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28670152 #FOAvip English Wikipedia <ref>{{Cite pmid|28670152 }}</ref> Deep Learning in Medical Imaging: General Overview #MMPMID28670152 Lee JG ; Jun S ; Cho YW ; Lee H ; Kim GB ; Seo JB ; Kim N Korean J Radiol 2017[Jul]; 18 (4 ): 570-584 PMID28670152 show ga The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging. |*Algorithms [MESH] |*Neural Networks, Computer [MESH] |Humans [MESH] |Image Processing, Computer-Assisted [MESH] |Knee/diagnostic imaging [MESH] |Magnetic Resonance Imaging [MESH]Deep Learning in Medical Imaging: General Overview (epmc).pdf DeepDyve Pubget Overpricing