LEADER 04758nam 2201429z- 450 001 9910557435103321 005 20231214133029.0 035 $a(CKB)5400000000043372 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76739 035 $a(EXLCZ)995400000000043372 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning in Medical Image Analysis 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (458 p.) 311 $a3-0365-1469-4 311 $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 $ainterpretable/explainable machine learning 610 $aimage classification 610 $aimage processing 610 $amachine learning models 610 $awhite box 610 $ablack box 610 $acancer prediction 610 $adeep learning 610 $amultimodal learning 610 $aconvolutional neural networks 610 $aautism 610 $afMRI 610 $atexture analysis 610 $amelanoma 610 $aglcm matrix 610 $amachine learning 610 $aclassifiers 610 $aexplainability 610 $aexplainable AI 610 $aXAI 610 $amedical imaging 610 $adiagnosis 610 $aARMD 610 $achange detection 610 $aunsupervised learning 610 $amicrowave breast imaging 610 $aimage reconstruction 610 $atumor detection 610 $adigital pathology 610 $awhole slide image processing 610 $amultiple instance learning 610 $adeep learning classification 610 $aHER2 610 $amedical images 610 $atransfer learning 610 $aoptimizers 610 $aneo-adjuvant treatment 610 $atumour cellularity 610 $acancer 610 $abreast cancer 610 $adiagnostics 610 $aimaging 610 $acomputation 610 $aartificial intelligence 610 $a3D segmentation 610 $aactive surface 610 $adiscriminant analysis 610 $aPET imaging 610 $amedical image analysis 610 $abrain tumor 610 $acervical cancer 610 $acolon cancer 610 $alung cancer 610 $acomputer vision 610 $amusculoskeletal images 610 $alung disease detection 610 $ataxonomy 610 $aconvolutional neural network 610 $aCycleGAN 610 $adata augmentation 610 $adermoscopic images 610 $adomain transfer 610 $amacroscopic images 610 $askin lesion segmentation 610 $ainfection detection 610 $aCOVID-19 610 $aX-ray images 610 $abayesian inference 610 $ashifted-scaled dirichlet distribution 610 $aMCMC 610 $agibbs sampling 610 $aobject detection 610 $asurgical tools 610 $aopen surgery 610 $aegocentric camera 610 $acomputers in medicine 610 $asegmentation 610 $aMRI 610 $aECG signal detection 610 $aportable monitoring devices 610 $a1D-convolutional neural network 610 $amedical image segmentation 610 $adomain adaptation 610 $ameta-learning 610 $aU-Net 610 $acomputed tomography (CT) 610 $amagnetic resonance imaging (MRI) 610 $alow-dose 610 $asparse-angle 610 $aquantitative comparison 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