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020			_a9783030716769
024	7		_a10.1007/978-3-030-71676-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		1	_aR859.7.A78 _b2021 EB
245	0	0	_aDeep Learning for Biomedical Data Analysis . _bTechniques, Approaches, and Applications _cedited by Mourad Elloumi
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
264		4	_c2021
300			_a1 recurso en línea (VI, 359 páginas) _b130 ilustraciones, 40 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aBiomedical and Life Sciences (SpringerNature-11642)
490	0		_aBiomedical and Life Sciences (R0) (SpringerNature-43708)
505	0		_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.
520	3		_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.
988			_aSpringer_BiomedLife_2021
650		7	_2embne _9421371 _aInteligencia artificial en medicina
650		7	_2embne _9140931 _aBiotecnología
700	1		_aElloumi, Mourad. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030716752
776	0	8	_iPrinted edition: _z9783030716776
776	0	8	_iPrinted edition: _z9783030716783
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-71676-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _ek _zSI