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10.1146/annurev-bioeng-071516-044442 http://scihub22266oqcxt.onion/10.1146/annurev-bioeng-071516-044442 C5479722!5479722 !28301734     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28301734 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28301734 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Annu+Rev+Biomed+Eng 2017 ; 19 (ä): 221-248 Nephropedia Template TP {{tp|p=28301734 |t=2017. Deep Learning in Medical Image Analysis |pdf=|usr=}}{{28301734 }} gab.com Text Deep Learning in Medical Image Analysis JO: Annu Rev Biomed Eng http://www.kidney.de/pget.php?&tw=1&pmid=28301734 #FOAvip This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement. Twit Text FOAVip Deep Learning in Medical Image Analysis ????Annu Rev Biomed Eng http://www.kidney.de/pget.php?&tw=1&pmid=28301734 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28301734 #FOAvip English Wikipedia <ref>{{Cite pmid|28301734 }}</ref> Deep Learning in Medical Image Analysis #MMPMID28301734 Shen D ; Wu G ; Suk HI Annu Rev Biomed Eng 2017[Jun]; 19 (ä): 221-248 PMID28301734 show ga This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement. |*Algorithms [MESH] |*Neural Networks, Computer [MESH] |*Unsupervised Machine Learning [MESH] |Diagnostic Imaging/*methods [MESH] |Image Enhancement/methods [MESH] |Image Interpretation, Computer-Assisted/*methods [MESH]Deep Learning in Medical Image Analysis (epmc).pdf DeepDyve Pubget Overpricing