Machine Learning and Medical Imaging
2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Guorong [verfasserIn] Shen, Dinggang [beteiligter] Sabuncu, Mert [beteiligter] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Place of publication not identified s.l.: Elsevier Ltd ; 2016 |
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| Anmerkung: |
Includes index |
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| Umfang: |
1 Online-Ressource |
| Reproduktion: |
Online-Ausg. |
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| Reihe: |
The Elsevier and MICCAI society book series |
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| Links: | |
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| ISBN: |
0-12-804114-5 978-0-12-804114-7 |
| Katalog-ID: |
879398892 |
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| 520 | |a 2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space | ||
| 520 | |a 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection | ||
| 520 | |a Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs | ||
| 520 | |a Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis | ||
| 520 | |a Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations | ||
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0128041145 : electronic bk. 0-12-804114-5 9780128041147 : electronic bk. 978-0-12-804114-7 (DE-627)879398892 (DE-576)9879398890 (DE-599)GBV879398892 (OCoLC)956998930 (ELSEVIER)ocn956998930 (ELSEVIER)ocn956988809 (EBP)003089657 DE-627 eng DE-627 rda eng XA-GB RC78.7.D53 MED 045000 bisacsh MED 014000 bisacsh MED 022000 bisacsh MED 112000 bisacsh HEA 039000 bisacsh Wu, Guorong verfasserin aut Machine Learning and Medical Imaging [Place of publication not identified] s.l. Elsevier Ltd 2016 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Elsevier and MICCAI society book series Includes index 2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations Online-Ausg. Diagnostic imaging Digital techniques Artificial intelligence Medical applications Diagnostic imaging Digital techniques Artificial intelligence Medical applications Image Processing, Computer-Assisted Machine Learning HEALTH & FITNESS ; Diseases ; General MEDICAL ; Clinical Medicine MEDICAL ; Diseases MEDICAL ; Evidence-Based Medicine MEDICAL ; Internal Medicine Artificial intelligence ; Medical applications Diagnostic imaging ; Digital techniques Intelligence artificielle en médecine (CaQQLa)201-0180593 Imagerie pour le diagnostic - Techniques numériques (CaQQLa)201-0395388 Electronic book Shen, Dinggang beteiligter asn Sabuncu, Mert beteiligter asn 9780128040768 Erscheint auch als Druck-Ausgabe 9780128040768 http://www.sciencedirect.com/science/book/9780128040768 X:ELSEVIER Verlag lizenzpflichtig Volltext https://www.sciencedirect.com/science/book/9780128040768 X:ELSEVIER Verlag lizenzpflichtig http://www.gbv.de/dms/bowker/toc/9780128040768.pdf V:DE-601 X:Bowker pdf/application 2017-02-24 Verlag Inhaltsverzeichnis Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom-BL ZDB-33-EBS ZDB-33-EGE 2016 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP GBV_ILN_105 ISIL_DE-841 GBV_ILN_120 ISIL_DE-715 GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 616.07/54 23 01 0830 168753943X ACQ Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots i z 17-05-17 105 01 0841 1787625087 05 --%%-- KaufEBook202103 --%%-- --%%-- OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-07-18 120 01 0715 4510358997 00 --%%-- --%%-- g --%%-- alma z 11-04-24 132 01 0959 4499992257 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514712965 OLR-EBS Vervielfältigungen (z.B. 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| spelling |
0128041145 : electronic bk. 0-12-804114-5 9780128041147 : electronic bk. 978-0-12-804114-7 (DE-627)879398892 (DE-576)9879398890 (DE-599)GBV879398892 (OCoLC)956998930 (ELSEVIER)ocn956998930 (ELSEVIER)ocn956988809 (EBP)003089657 DE-627 eng DE-627 rda eng XA-GB RC78.7.D53 MED 045000 bisacsh MED 014000 bisacsh MED 022000 bisacsh MED 112000 bisacsh HEA 039000 bisacsh Wu, Guorong verfasserin aut Machine Learning and Medical Imaging [Place of publication not identified] s.l. Elsevier Ltd 2016 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Elsevier and MICCAI society book series Includes index 2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations Online-Ausg. Diagnostic imaging Digital techniques Artificial intelligence Medical applications Diagnostic imaging Digital techniques Artificial intelligence Medical applications Image Processing, Computer-Assisted Machine Learning HEALTH & FITNESS ; Diseases ; General MEDICAL ; Clinical Medicine MEDICAL ; Diseases MEDICAL ; Evidence-Based Medicine MEDICAL ; Internal Medicine Artificial intelligence ; Medical applications Diagnostic imaging ; Digital techniques Intelligence artificielle en médecine (CaQQLa)201-0180593 Imagerie pour le diagnostic - Techniques numériques (CaQQLa)201-0395388 Electronic book Shen, Dinggang beteiligter asn Sabuncu, Mert beteiligter asn 9780128040768 Erscheint auch als Druck-Ausgabe 9780128040768 http://www.sciencedirect.com/science/book/9780128040768 X:ELSEVIER Verlag lizenzpflichtig Volltext https://www.sciencedirect.com/science/book/9780128040768 X:ELSEVIER Verlag lizenzpflichtig http://www.gbv.de/dms/bowker/toc/9780128040768.pdf V:DE-601 X:Bowker pdf/application 2017-02-24 Verlag Inhaltsverzeichnis Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom-BL ZDB-33-EBS ZDB-33-EGE 2016 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP GBV_ILN_105 ISIL_DE-841 GBV_ILN_120 ISIL_DE-715 GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 616.07/54 23 01 0830 168753943X ACQ Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots i z 17-05-17 105 01 0841 1787625087 05 --%%-- KaufEBook202103 --%%-- --%%-- OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-07-18 120 01 0715 4510358997 00 --%%-- --%%-- g --%%-- alma z 11-04-24 132 01 0959 4499992257 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514712965 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540274945 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520360056 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046053208 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 23 01 0830 Elsevier EBook http://www.sciencedirect.com/science/book/9780128040768 105 01 0841 http://www.sciencedirect.com/science/book/9780128040768 120 01 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991016034891003501 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128040768 185 01 3519 http://www.sciencedirect.com/science/book/9780128040768 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128040768 2020 01 DE-Ch1 http://www.sciencedirect.com/science/book/9780128040768 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128040768 132 01 0959 00 EBooks Elsevier Engineering 23 01 0830 2016-02211, 2016-02212, 2016-02213, 2016-02214, 2016-02215 23 01 0830 ACQ 23 01 0830 olr-else 23 01 0830 olr-else2 105 01 0841 OLR-ELV-TEST 120 01 0715 alma 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier 23 01 0830 2017.05.17 |
| allfields_unstemmed |
0128041145 : electronic bk. 0-12-804114-5 9780128041147 : electronic bk. 978-0-12-804114-7 (DE-627)879398892 (DE-576)9879398890 (DE-599)GBV879398892 (OCoLC)956998930 (ELSEVIER)ocn956998930 (ELSEVIER)ocn956988809 (EBP)003089657 DE-627 eng DE-627 rda eng XA-GB RC78.7.D53 MED 045000 bisacsh MED 014000 bisacsh MED 022000 bisacsh MED 112000 bisacsh HEA 039000 bisacsh Wu, Guorong verfasserin aut Machine Learning and Medical Imaging [Place of publication not identified] s.l. Elsevier Ltd 2016 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Elsevier and MICCAI society book series Includes index 2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations Online-Ausg. Diagnostic imaging Digital techniques Artificial intelligence Medical applications Diagnostic imaging Digital techniques Artificial intelligence Medical applications Image Processing, Computer-Assisted Machine Learning HEALTH & FITNESS ; Diseases ; General MEDICAL ; Clinical Medicine MEDICAL ; Diseases MEDICAL ; Evidence-Based Medicine MEDICAL ; Internal Medicine Artificial intelligence ; Medical applications Diagnostic imaging ; Digital techniques Intelligence artificielle en médecine (CaQQLa)201-0180593 Imagerie pour le diagnostic - Techniques numériques (CaQQLa)201-0395388 Electronic book Shen, Dinggang beteiligter asn Sabuncu, Mert beteiligter asn 9780128040768 Erscheint auch als Druck-Ausgabe 9780128040768 http://www.sciencedirect.com/science/book/9780128040768 X:ELSEVIER Verlag lizenzpflichtig Volltext https://www.sciencedirect.com/science/book/9780128040768 X:ELSEVIER Verlag lizenzpflichtig http://www.gbv.de/dms/bowker/toc/9780128040768.pdf V:DE-601 X:Bowker pdf/application 2017-02-24 Verlag Inhaltsverzeichnis Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom-BL ZDB-33-EBS ZDB-33-EGE 2016 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP GBV_ILN_105 ISIL_DE-841 GBV_ILN_120 ISIL_DE-715 GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 616.07/54 23 01 0830 168753943X ACQ Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots i z 17-05-17 105 01 0841 1787625087 05 --%%-- KaufEBook202103 --%%-- --%%-- OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-07-18 120 01 0715 4510358997 00 --%%-- --%%-- g --%%-- alma z 11-04-24 132 01 0959 4499992257 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514712965 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540274945 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520360056 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046053208 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 23 01 0830 Elsevier EBook http://www.sciencedirect.com/science/book/9780128040768 105 01 0841 http://www.sciencedirect.com/science/book/9780128040768 120 01 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991016034891003501 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128040768 185 01 3519 http://www.sciencedirect.com/science/book/9780128040768 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128040768 2020 01 DE-Ch1 http://www.sciencedirect.com/science/book/9780128040768 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128040768 132 01 0959 00 EBooks Elsevier Engineering 23 01 0830 2016-02211, 2016-02212, 2016-02213, 2016-02214, 2016-02215 23 01 0830 ACQ 23 01 0830 olr-else 23 01 0830 olr-else2 105 01 0841 OLR-ELV-TEST 120 01 0715 alma 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier 23 01 0830 2017.05.17 |
| allfieldsGer |
0128041145 : electronic bk. 0-12-804114-5 9780128041147 : electronic bk. 978-0-12-804114-7 (DE-627)879398892 (DE-576)9879398890 (DE-599)GBV879398892 (OCoLC)956998930 (ELSEVIER)ocn956998930 (ELSEVIER)ocn956988809 (EBP)003089657 DE-627 eng DE-627 rda eng XA-GB RC78.7.D53 MED 045000 bisacsh MED 014000 bisacsh MED 022000 bisacsh MED 112000 bisacsh HEA 039000 bisacsh Wu, Guorong verfasserin aut Machine Learning and Medical Imaging [Place of publication not identified] s.l. Elsevier Ltd 2016 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Elsevier and MICCAI society book series Includes index 2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations Online-Ausg. Diagnostic imaging Digital techniques Artificial intelligence Medical applications Diagnostic imaging Digital techniques Artificial intelligence Medical applications Image Processing, Computer-Assisted Machine Learning HEALTH & FITNESS ; Diseases ; General MEDICAL ; Clinical Medicine MEDICAL ; Diseases MEDICAL ; Evidence-Based Medicine MEDICAL ; Internal Medicine Artificial intelligence ; Medical applications Diagnostic imaging ; Digital techniques Intelligence artificielle en médecine (CaQQLa)201-0180593 Imagerie pour le diagnostic - Techniques numériques (CaQQLa)201-0395388 Electronic book Shen, Dinggang beteiligter asn Sabuncu, Mert beteiligter asn 9780128040768 Erscheint auch als Druck-Ausgabe 9780128040768 http://www.sciencedirect.com/science/book/9780128040768 X:ELSEVIER Verlag lizenzpflichtig Volltext https://www.sciencedirect.com/science/book/9780128040768 X:ELSEVIER Verlag lizenzpflichtig http://www.gbv.de/dms/bowker/toc/9780128040768.pdf V:DE-601 X:Bowker pdf/application 2017-02-24 Verlag Inhaltsverzeichnis Inhaltsverzeichnis BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom-BL ZDB-33-EBS ZDB-33-EGE 2016 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP GBV_ILN_105 ISIL_DE-841 GBV_ILN_120 ISIL_DE-715 GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 616.07/54 23 01 0830 168753943X ACQ Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots i z 17-05-17 105 01 0841 1787625087 05 --%%-- KaufEBook202103 --%%-- --%%-- OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-07-18 120 01 0715 4510358997 00 --%%-- --%%-- g --%%-- alma z 11-04-24 132 01 0959 4499992257 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514712965 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540274945 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520360056 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 4046053208 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 23 01 0830 Elsevier EBook http://www.sciencedirect.com/science/book/9780128040768 105 01 0841 http://www.sciencedirect.com/science/book/9780128040768 120 01 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991016034891003501 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128040768 185 01 3519 http://www.sciencedirect.com/science/book/9780128040768 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128040768 2020 01 DE-Ch1 http://www.sciencedirect.com/science/book/9780128040768 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128040768 132 01 0959 00 EBooks Elsevier Engineering 23 01 0830 2016-02211, 2016-02212, 2016-02213, 2016-02214, 2016-02215 23 01 0830 ACQ 23 01 0830 olr-else 23 01 0830 olr-else2 105 01 0841 OLR-ELV-TEST 120 01 0715 alma 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier 23 01 0830 2017.05.17 |
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0128041145 : electronic bk. 0-12-804114-5 9780128041147 : electronic bk. 978-0-12-804114-7 (DE-627)879398892 (DE-576)9879398890 (DE-599)GBV879398892 (OCoLC)956998930 (ELSEVIER)ocn956998930 (ELSEVIER)ocn956988809 (EBP)003089657 DE-627 eng DE-627 rda eng XA-GB RC78.7.D53 MED 045000 bisacsh MED 014000 bisacsh MED 022000 bisacsh MED 112000 bisacsh HEA 039000 bisacsh Wu, Guorong verfasserin aut Machine Learning and Medical Imaging [Place of publication not identified] s.l. Elsevier Ltd 2016 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Elsevier and MICCAI society book series Includes index 2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations Online-Ausg. 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2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations Includes index |
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2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations Includes index |
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2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space 3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 1.3 Mixture Model; 1.3.1 Model; 1.3.2 Inference; 1.4 Markov Random Field Model; 1.4.1 Model; 1.4.2 Inference; 1.5 Summary; References; Chapter 2: Kernel machine regression in neuroimaging genetics; 2.1 Introduction; 2.2 Mathematical Foundations Includes index |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a22002652 4500</leader><controlfield tag="001">879398892</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240503140440.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">170209s2016 xxk|||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0128041145</subfield><subfield code="c">: electronic bk.</subfield><subfield code="9">0-12-804114-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780128041147</subfield><subfield code="c">: electronic bk.</subfield><subfield code="9">978-0-12-804114-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)879398892</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-576)9879398890</subfield></datafield><datafield tag="035" 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code="a">s.l.</subfield><subfield code="b">Elsevier Ltd</subfield><subfield code="c">2016</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">The Elsevier and MICCAI society book series</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes index</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">2.2.1 From Regression Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 Convolutional encoder network segmentation; 3.3.3 Modeling Disease Variability; 3.4 Future Research Needs; Acknowledgments; References; Chapter 4: Machine learning and its application in microscopic image analysis; 4.1 Introduction; 4.2 Detection</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Appendix A.2: Kernel Function and Kernel MatrixAppendix A.3: Reproducing Kernel Hilbert Space; Appendix A.4: Mercer's Theorem; Appendix A.5: Representer Theorem; Appendix B: Restricted Maximum Likelihood Estimation; References; Chapter 3: Deep learning of brain images and its application to multiple sclerosis; 3.1 Introduction; 3.1.1 Learning From Unlabeled Input Images; 3.1.1.1 From restricted Boltzmann machines to deep belief networks; Inference; Training; Deep belief networks; 3.1.1.2 Variants of restricted Boltzmann machines and deep belief networks; Convolutional DBNs</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Alternative unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 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Cutting-edge Machine Learning Techniques in Medical Imaging Functional Connectivity Parcellation of Human Brain Kernel Machine Regression Neuroimaging Genetics Deep Learning of Brain Images and its Application to Multiple Sclerosis Machine Learning and Its Application in Microscopic Image Analysis Sparse Models for Imaging Genetics Dictionary Learning for Medical Image Denoising, Reconstruction and Segmentation Advanced Sparsity Techniques in Magnetic Resonance Imaging Hashing-based Large-Scale Medical Image Retrieval for Computer Aided Diagnosis Successful Applications in Medical Imaging Multi-template based Multi-view Learning for Alzheimer's Disease Diagnosis Machine Learning as a Means towards Precision Diagnostics and Prognostics Learning and Predicting Respiratory Motion from 4D CT Lung Images Learning Pathological Deviation from a Normal Pattern of Myocardial Motion: Added-Value for CRT Studies? From Point to Surface: Hierarchical Parsing of Human Anatomy in Medical Images Using Machine Learning Technologies Machine Learning in Brain Imaging Genomics Holistic Atlases of Functional Networks and Interactions (HAFNI Neuronal Network Architecture and Temporal Lobe Epilepsy: A Connectome Based and Machine Learning Study |
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Analysis to Kernel Methods2.2.2 Kernel Machine Regression; 2.2.3 Linear Mixed Effects Models; 2.2.4 Statistical Inference; 2.2.5 Constructing and Selecting Kernels; 2.2.6 Theoretical Extensions; 2.2.6.1 Generalized kernel machine regression; 2.2.6.2 Multiple kernel functions; 2.2.6.3 Correlated phenotypes; 2.2.6.4 Multidimensional traits; 2.3 Applications; 2.3.1 Genetic Association Studies; 2.3.2 Imaging Genetics; 2.4 Conclusion and Future Directions; Acknowledgments; Appendix A: Reproducing Kernel Hilbert Spaces; Appendix A.1: Inner Product and Hilbert Space</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">3.2.3.3 Task identification using functional MRI dataset3.2.3.4 Early diagnosis of Alzheimer's disease; 3.2.3.5 High-level 3D PET image feature learning; 3.3 Focus on Deep Learning in Multiple Sclerosis; 3.3.1 Multiple Sclerosis and the Role of Imaging; 3.3.2 White Matter Lesion Segmentation; 3.3.2.1 Patch-based segmentation methods; 3.3.2.2 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unit types3.1.1.3 Stacked denoising autoencoders; 3.1.2 Learning From Labeled Input Images; 3.1.2.1 Dense neural networks; 3.1.2.2 Convolutional neural networks; 3.2 Overview of Deep Learning in Neuroimaging; 3.2.1 Deformable Image Registration Using Deep-Learned Features; 3.2.2 Segmentation of Neuroimaging Data Using Deep Learning; 3.2.2.1 Hippocampus segmentation; 3.2.2.2 Infant brain image segmentation; 3.2.2.3 Brain tumor segmentation; 3.2.3 Classification of Neuroimaging Data Using Deep Learning; 3.2.3.1 Schizophrenia diagnosis; 3.2.3.2 Huntington disease diagnosis</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Front Cover; Machine Learning and Medical Imaging; Copyright; Contents; Contributors; Editor Biographies; Preface; Acknowledgments; Part 1: Cutting-edge machine learning techniques in medical imaging; Chapter 1: Functional connectivity parcellation of the human brain; 1.1 Introduction; 1.2 Approaches to Connectivity-Based Brain Parcellation; 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