LEADER 03848nam 2200733 450 001 9910463220903321 005 20200520144314.0 010 $a1-57922-876-3 010 $a1-57922-877-1 035 $a(CKB)2670000000316909 035 $a(EBL)1108388 035 $a(OCoLC)823718983 035 $a(SSID)ssj0000832641 035 $a(PQKBManifestationID)12357978 035 $a(PQKBTitleCode)TC0000832641 035 $a(PQKBWorkID)10935274 035 $a(PQKB)11669389 035 $a(MiAaPQ)EBC1108388 035 $a(MiAaPQ)EBC4438561 035 $a(Au-PeEL)EBL4438561 035 $a(CaPaEBR)ebr11170603 035 $a(OCoLC)884586084 035 $a(EXLCZ)992670000000316909 100 $a20160413h20132013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAlternative pathways to the baccalaureate $edo community colleges offer a viable solution to the nation's knowledge deficit? /$fedited by Nancy Remington and Ronald Remington ; foreword by Carol D'Amico 210 1$aSterling, Virginia :$cStylus,$d2013. 210 4$dİ2013 215 $a1 online resource (378 p.) 300 $a"Published in association with the Community College Baccalaureate Association." 311 $a1-57922-874-7 320 $aIncludes bibliographical references and index. 327 $aCover Page; Title Page; Copyright Page; Dedication; Contents; ACKNOWLEDGMENTS; FOREWORD; INTRODUCTION; PART ONE: NEEDS, IMPLICATIONS, AND POLITICS; 1. THE HISTORY OF THE COMMUNITY COLLEGE BACCALAUREATE MOVEMENT; 2. THE COMMUNITY COLLEGE BACCALAUREATE; 3. MISSION METAMORPHOSIS; 4. STUDENT VOICES; 5. IMPACT ON ACCREDITATION STATUS WHEN COMMUNITY COLLEGES OFFER BACCALAUREATE DEGREES; 6. UPDATE ON THE COMMUNITY COLLEGE BACCALAUREATE; PART TWO: MODELS AND CONTEXTS; 7. THE BACCALAUREATE MOVEMENT IN FLORIDA; 8. APPLIED BACCALAUREATE DEGREES IN THE CONTEXT OF BACCALAUREATE EDUCATION 327 $a9. REFLECTIONS ON THE NATURE AND STATUS OF THE APPLIED BACCALAUREATE DEGREE10. A VIRTUAL PATHWAY TO BACCALAUREATE COMPLETION; 11. THE UNIVERSITY PARTNERSHIP AT LORAIN COUNTY COMMUNITY COLLEGE; 12. THE COMPREHENSIVE COLLEGE BACCALAUREATE; ABOUT THE CONTRIBUTORS; INDEX 330 $aThe premise of this book is that, in a globalized economy dependent on innovation and knowledge, higher education must provide greater, more affordable access to the acquisition of higher-level skills and knowledge for a greater proportion of the population.The purpose of this book is to open up a debate about the status quo. Should four-year institutions remain the near-exclusive conferrers of the baccalaureate? Or is there a legitimate role for community colleges who already educate over half the undergraduate population of the United States, at lower cost with few barriers to access?The con 606 $aCommunity colleges$zUnited States 606 $aCommunity colleges$zCanada 606 $aCommunity colleges$xCurricula$zUnited States 606 $aCommunity colleges$xCurricula$zCanada 606 $aBachelor of arts degree$zUnited States 606 $aBachelor of arts degree$zCanada 608 $aElectronic books. 615 0$aCommunity colleges 615 0$aCommunity colleges 615 0$aCommunity colleges$xCurricula 615 0$aCommunity colleges$xCurricula 615 0$aBachelor of arts degree 615 0$aBachelor of arts degree 676 $a378.1/5430973 702 $aRemington$b Nancy$f1948- 702 $aRemington$b Ronald$f1941- 702 $aD'Amico$b Carol 712 02$aCommunity College Baccalaureate Association. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910463220903321 996 $aAlternative pathways to the baccalaureate$91987826 997 $aUNINA LEADER 04885nam 2201189z- 450 001 9910557353503321 005 20220111 035 $a(CKB)5400000000042355 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77043 035 $a(oapen)doab77043 035 $a(EXLCZ)995400000000042355 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvanced Computational Methods for Oncological Image Analysis 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (262 p.) 311 08$a3-0365-2554-8 311 08$a3-0365-2555-6 330 $a[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians' unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations-such as segmentation, co-registration, classification, and dimensionality reduction-and multi-omics data integration.] 606 $aMedicine and Nursing$2bicssc 610 $a3D-CNN 610 $abone scintigraphy 610 $abrain MRI image 610 $abrain tumor 610 $abrain tumor segmentation 610 $aBRATS dataset 610 $abreast cancer 610 $abreast cancer detection 610 $abreast cancer diagnosis 610 $abreast imaging 610 $abreast mass 610 $aclassification 610 $aclutter rejection 610 $acomputer-aided detection 610 $acontrast source inversion 610 $adataset partition 610 $adeep learning 610 $adimensionality reduction 610 $aensemble classification 610 $aensemble method 610 $afalse positives reduction 610 $afeature selection 610 $aimage reconstruction 610 $aimaging biomarkers 610 $aimmunotherapy 610 $aincoherent imaging 610 $ainterferometric optical fibers 610 $ak-means clustering 610 $aKolmogorov-Smirnov hypothesis test 610 $amachine learning 610 $amagnetic resonance imaging 610 $amammography 610 $aMask R-CNN 610 $amass detection 610 $amass segmentation 610 $amedical imaging 610 $amelanoma detection 610 $amicrowave imaging 610 $aMRgFUS 610 $an/a 610 $aperformance metrics 610 $aprincipal component analysis 610 $aprostate cancer 610 $aproton resonance frequency shift 610 $aradiomics 610 $aRBF neural networks 610 $areferenceless thermometry 610 $aregion growing 610 $arisk assessment 610 $asegmentation 610 $aself-attention 610 $asemisupervised classification 610 $ashallow machine learning 610 $askull stripping 610 $astatistical inference 610 $asurvey 610 $atemperature variations 610 $atexture 610 $atransfer learning 610 $atumor region 610 $aU-Net 610 $aunsupervised machine learning 610 $aWisconsin Breast Cancer Dataset 615 7$aMedicine and Nursing 700 $aRundo$b Leonardo$4edt$01290017 702 $aMilitello$b Carmelo$4edt 702 $aConti$b Vincenzo$4edt 702 $aZaccagna$b Fulvio$4edt 702 $aHan$b Changhee$4edt 702 $aRundo$b Leonardo$4oth 702 $aMilitello$b Carmelo$4oth 702 $aConti$b Vincenzo$4oth 702 $aZaccagna$b Fulvio$4oth 702 $aHan$b Changhee$4oth 906 $aBOOK 912 $a9910557353503321 996 $aAdvanced Computational Methods for Oncological Image Analysis$93021302 997 $aUNINA