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001438119 001__ 1438119
001438119 003__ OCoLC
001438119 005__ 20230309004249.0
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001438119 019__ $$a1261365578$$a1266811306$$a1268573816$$a1284943500
001438119 020__ $$a9783030716769$$q(electronic bk.)
001438119 020__ $$a3030716767$$q(electronic bk.)
001438119 020__ $$z9783030716752
001438119 020__ $$z3030716759
001438119 0247_ $$a10.1007/978-3-030-71676-9$$2doi
001438119 035__ $$aSP(OCoLC)1260292821
001438119 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dDCT$$dOCLCF$$dUKAHL$$dN$T$$dOCLCO$$dDKU$$dOCLCQ$$dCOM$$dOCLCO$$dOCLCQ
001438119 049__ $$aISEA
001438119 050_4 $$aR859.7.A78$$bD44 2021
001438119 08204 $$a610.285/63$$223
001438119 24500 $$aDeep learning for biomedical data analysis :$$btechniques, approaches, and applications /$$cMourad Elloumi, editor.
001438119 264_1 $$aCham :$$bSpringer,$$c[2021]
001438119 264_4 $$c©2021
001438119 300__ $$a1 online resource :$$billustrations (some color)
001438119 336__ $$atext$$btxt$$2rdacontent
001438119 337__ $$acomputer$$bc$$2rdamedia
001438119 338__ $$aonline resource$$bcr$$2rdacarrier
001438119 347__ $$atext file
001438119 347__ $$bPDF
001438119 500__ $$aIncludes index.
001438119 5050_ $$a1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Data -- Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues -- A Deep Learning Model for MicroRNA-Target Binding -- Recurrent Neural Networks Architectures for Accidental Fall Detection on Wearable Embedded Devices -- Medical Image Retrieval System using Deep Learning Techniques -- Medical Image Fusion using Deep Learning -- Deep Learning for Histopathological Image Analysis -- Innovative Deep Learning Approach for Biomedical Data Instantiation and Visualization -- Convolutional Neural Networks in Advanced Biomedical Imaging Applications -- Deep Learning for Lung Disease Detection from Chest X-Rays Images -- Deep Learning in Multi-Omics Data Integration in Cancer Diagnostic -- Using Deep Learning with Canadian Primary Care Data for Disease Diagnosis -- Brain Tumor Segmentation and Surveillance with Deep Artificial Neural Networks.
001438119 506__ $$aAccess limited to authorized users.
001438119 520__ $$aThis book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
001438119 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 3, 2021).
001438119 650_0 $$aArtificial intelligence$$xMedical applications.
001438119 650_0 $$aArtificial intelligence$$xBiological applications.
001438119 650_6 $$aIntelligence artificielle en médecine.
001438119 650_6 $$aIntelligence artificielle$$xApplications biologiques.
001438119 655_0 $$aElectronic books.
001438119 7001_ $$aElloumi, Mourad,$$eeditor.
001438119 77608 $$iPrint version:$$z3030716759$$z9783030716752$$w(OCoLC)1237633963
001438119 852__ $$bebk
001438119 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-71676-9$$zOnline Access$$91397441.1
001438119 909CO $$ooai:library.usi.edu:1438119$$pGLOBAL_SET
001438119 980__ $$aBIB
001438119 980__ $$aEBOOK
001438119 982__ $$aEbook
001438119 983__ $$aOnline
001438119 994__ $$a92$$bISE