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008			200206s2020 gw | o |||| 0|eng d
020			_a9783030331283
024	7		_a10.1007/978-3-030-33128-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
041	0		_aeng
050		4	_aR859.7.A78 _b2020 EB
245	0	0	_aDeep Learning in Medical Image Analysis : _bChallenges and Applications _cedited by Gobert Lee, Hiroshi Fujita
250			_aFirst edition 2020
264		1	_aCham, Switzerland _bSpringer International Publishing _c2020
300			_a1 recurso en línea (VIII, 181 páginas) _b131 ilustraciones, 114 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aAdvances in Experimental Medicine and Biology _x0065-2598 _v1213
490	0		_aBiomedical and Life Sciences (Springer-11642)
505	0		_aDeep Learning in Medical Image Analysis -- Medical Image Synthesis via Deep Learning -- Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation -- Deep Learning Computer Aided Diagnosis for Breast Lesion in Digital Mammogram -- Decision support system for lung cancer using PET/CT and microscopic images -- Lesion Image Synthesis using DCGANs for Metastatic Liver Cancer Detection -- Retinopathy analysis based on deep convolution neural network -- Diagnosis of Glaucoma on retinal fundus images using deep learning: detection of nerve fiber layer defect and optic disc analysis -- Automatic segmentation of multiple organs on 3D CT images by using deep learning approaches -- Techniques and Applications in Skin OCT Analysis -- Deep Learning Technique for Musculoskeletal Analysis -- Index.
520	3		_aThis book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.
988			_aSpringer_BiomedLife_31032020
650		7	_2embne _aInteligencia artificial _xAplicaciones médicas _9413115
650		7	_2embne _9143820 _aIngeniería biomédica
700	1		_aLee, Gobert. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFujita, Hiroshi. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
776	0	8	_iPrinted edition: _z9783030331276
776	0	8	_iPrinted edition: _z9783030331290
776	0	8	_iPrinted edition: _z9783030331306
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://dx.doi.org/10.1007/978-3-030-33128-3
942			_2lcc _cLE _n0
998			_b04/2020 _dz _eb _zSI