LEADER 02011 am 2200577 n 450 001 9910418053903321 005 20240220213621.0 010 $a979-1-03-654015-8 024 7 $a10.4000/books.cemca.6042 035 $a(CKB)4100000010014030 035 $a(FrMaCLE)OB-cemca-6042 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/41291 035 $a(PPN)241685133 035 $a(EXLCZ)994100000010014030 100 $a20191219j|||||||| ||| 0 101 0 $aspa 135 $auu||||||m|||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArquitectura y Arqueología $eMetodologías en la cronología de Yucatán /$fPaul Gendrop 210 $aMexico $cCentro de estudios mexicanos y centroamericanos$d2019 215 $a1 online resource (89 p.) 330 $aPonencias y contribuciones de reconocidos investigadores del área maya norte que intercambiaron reflexiones acerca de los estilos arquitectónicos de las zonas Río Bec, Chenes y Puuc ; así como cronología, estratigrafía o seriaciones arqueológicas. 606 $aArchaeology 606 $aarqueología 606 $aarquitectura 606 $aYucatán 610 $aarquitectura 610 $aYucatán 610 $aarqueología 615 4$aArchaeology 615 4$aarqueología 615 4$aarquitectura 615 4$aYucatán 700 $aAndrews$b George F$01286869 701 $aBall$b Joseph W$01286870 701 $aBenavides$b Antonio$0196137 701 $aBoucher$b Sylviane$01286871 701 $aCarrasco$b Ramón$01286872 701 $aFolan$b William J$01286873 701 $aGendrop$b Paul$033129 701 $aManzanilla$b Linda$01174827 701 $aMichelet$b Dominique$01286874 701 $aMills$b Lawrence$01286875 701 $aGendrop$b Paul$033129 801 0$bFR-FrMaCLE 906 $aBOOK 912 $a9910418053903321 996 $aArquitectura y Arqueología$93019969 997 $aUNINA LEADER 04795nam 2201453z- 450 001 9910557435103321 005 20220111 035 $a(CKB)5400000000043372 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76739 035 $a(oapen)doab76739 035 $a(EXLCZ)995400000000043372 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDeep Learning in Medical Image Analysis 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (458 p.) 311 08$a3-0365-1469-4 311 08$a3-0365-1470-8 330 $aThe accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis. 610 $a1D-convolutional neural network 610 $a3D segmentation 610 $aactive surface 610 $aARMD 610 $aartificial intelligence 610 $aautism 610 $abayesian inference 610 $ablack box 610 $abrain tumor 610 $abreast cancer 610 $acancer 610 $acancer prediction 610 $acervical cancer 610 $achange detection 610 $aclassifiers 610 $acolon cancer 610 $acomputation 610 $acomputed tomography (CT) 610 $acomputer vision 610 $acomputers in medicine 610 $aconvolutional neural network 610 $aconvolutional neural networks 610 $aCOVID-19 610 $aCycleGAN 610 $adata augmentation 610 $adeep learning 610 $adeep learning classification 610 $adermoscopic images 610 $adiagnosis 610 $adiagnostics 610 $adigital pathology 610 $adiscriminant analysis 610 $adomain adaptation 610 $adomain transfer 610 $aECG signal detection 610 $aegocentric camera 610 $aexplainability 610 $aexplainable AI 610 $afMRI 610 $agibbs sampling 610 $aglcm matrix 610 $aHER2 610 $aimage classification 610 $aimage processing 610 $aimage reconstruction 610 $aimaging 610 $ainfection detection 610 $ainterpretable/explainable machine learning 610 $alow-dose 610 $alung cancer 610 $alung disease detection 610 $amachine learning 610 $amachine learning models 610 $amacroscopic images 610 $amagnetic resonance imaging (MRI) 610 $aMCMC 610 $amedical image analysis 610 $amedical image segmentation 610 $amedical images 610 $amedical imaging 610 $amelanoma 610 $ameta-learning 610 $amicrowave breast imaging 610 $aMRI 610 $amultimodal learning 610 $amultiple instance learning 610 $amusculoskeletal images 610 $an/a 610 $aneo-adjuvant treatment 610 $aobject detection 610 $aopen surgery 610 $aoptimizers 610 $aPET imaging 610 $aportable monitoring devices 610 $aquantitative comparison 610 $asegmentation 610 $ashifted-scaled dirichlet distribution 610 $askin lesion segmentation 610 $asparse-angle 610 $asurgical tools 610 $ataxonomy 610 $atexture analysis 610 $atransfer learning 610 $atumor detection 610 $atumour cellularity 610 $aU-Net 610 $aunsupervised learning 610 $awhite box 610 $awhole slide image processing 610 $aX-ray images 610 $aXAI 700 $aZhang$b Yudong$4edt$0950777 702 $aGorriz$b Juan Manuel$4edt 702 $aDong$b Zhengchao$4edt 702 $aZhang$b Yudong$4oth 702 $aGorriz$b Juan Manuel$4oth 702 $aDong$b Zhengchao$4oth 906 $aBOOK 912 $a9910557435103321 996 $aDeep Learning in Medical Image Analysis$93023595 997 $aUNINA