LEADER 03983nam 22006615 450 001 9910483851403321 005 20200702040542.0 010 $a3-030-42750-1 024 7 $a10.1007/978-3-030-42750-4 035 $a(CKB)4100000011232686 035 $a(MiAaPQ)EBC6200126 035 $a(DE-He213)978-3-030-42750-4 035 $a(PPN)248395432 035 $a(EXLCZ)994100000011232686 100 $a20200515d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learners and Deep Learner Descriptors for Medical Applications /$fedited by Loris Nanni, Sheryl Brahnam, Rick Brattin, Stefano Ghidoni, Lakhmi C. Jain 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (286 pages) 225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v186 311 $a3-030-42748-X 320 $aIncludes bibliographical references. 330 $aThis book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects. . 410 0$aIntelligent Systems Reference Library,$x1868-4394 ;$v186 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aHealth informatics 606 $aBiomedical engineering 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 606 $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aHealth informatics. 615 0$aBiomedical engineering. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aHealth Informatics. 615 24$aBiomedical Engineering and Bioengineering. 676 $a610.28 702 $aNanni$b Loris$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBrahnam$b Sheryl$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBrattin$b Rick$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGhidoni$b Stefano$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJain$b Lakhmi C$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483851403321 996 $aDeep Learners and Deep Learner Descriptors for Medical Applications$92846134 997 $aUNINA