LEADER 01208nam a22002771i 4500 001 991003108079707536 005 20040604160458.0 008 040624s1966 rm a||||||||||||||||rum 035 $ab13025119-39ule_inst 035 $aARCHE-098279$9ExL 040 $aDip.to Beni Culturali$bita$cA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l. 082 04$a733.5 110 2 $aMuzeul de arheologie $0487234 245 10$aBronzuri figurate /$cMihai Irimia 260 $aConstanta :$bMuzeul regional de arheologie Dobrogea,$c1966 300 $a53 p. :$bill. ;$c21 cm 650 4$aBronzi romani$xRomania$xMuzeul regional de arheologie dobrogea 650 4$aRomania$xIconografia sacra 700 1 $aIrimia, Mihai$eauthor$4http://id.loc.gov/vocabulary/relators/aut$0734396 907 $a.b13025119$b02-04-14$c12-07-04 912 $a991003108079707536 945 $aLE001 AR V 94 8$g1$i2001000115655$lle001$nC. 1$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i13638695$z12-07-04 945 $aLE001 AR V 94 8° BIS$g1$i2001000161898$lle001$o-$pE10.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i14419105$z04-04-07 996 $aBronzuri figurate$91449729 997 $aUNISALENTO 998 $ale001$b12-07-04$cm$da $e-$frum$grm $h0$i1 LEADER 01894oam 2200529M 450 001 9910716410403321 005 20200213070600.9 035 $a(CKB)5470000002522046 035 $a(OCoLC)1065922798 035 $a(OCoLC)995470000002522046 035 $a(EXLCZ)995470000002522046 100 $a20071213d1927 ua 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAlfred St. Dennis. February 17, 1927. -- Committed to the Committee of the Whole House and ordered to be printed 210 1$a[Washington, D.C.] :$c[U.S. Government Printing Office],$d1927. 215 $a1 online resource (2 pages) 225 1 $aHouse report / 69th Congress, 2nd session. House ;$vno. 2151 225 1 $a[United States congressional serial set] ;$v[serial no. 8690] 300 $aBatch processed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aFDLP item number not assigned. 606 $aClaims 606 $aDesertion, Military 606 $aDesertion, Naval 606 $aLegislative amendments 606 $aMilitary discharge 606 $aDisabled veterans 607 $aUnited States$xHistory$yCivil War, 1861-1865 608 $aLegislative materials.$2lcgft 615 0$aClaims. 615 0$aDesertion, Military. 615 0$aDesertion, Naval. 615 0$aLegislative amendments. 615 0$aMilitary discharge. 615 0$aDisabled veterans. 701 $aWainwright$b Jonathan Mayhew$f1864-1945$pRepublican (NY)$01386848 801 0$bWYU 801 1$bWYU 801 2$bOCLCO 801 2$bOCLCQ 906 $aBOOK 912 $a9910716410403321 996 $aAlfred St. Dennis. February 17, 1927. -- Committed to the Committee of the Whole House and ordered to be printed$93484183 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