rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine learning and medical imaging
  name:
    name part: Wu, Guorong
    name part: (Researcher in medical imaging)
    role:
      role term: creator
  name:
    name part: Shen, Dinggang.
  name:
    name part: Sabuncu, Mert Rory
    name part: 1979-
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    place:
      place term: London, United Kingdom
    publisher: Academic Press is an imprint of Elsevier
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (514 pages).
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  note: Guorong Wu, Dinggang Shen, Mert R. Sabuncu.
  note: Includes bibliographical references and index.
  subject:
    topic: Diagnostic imaging
    topic: Digital techniques
  subject:
    topic: Artificial intelligence
    topic: Medical applications
  subject:
    topic: Image Processing, Computer-Assisted
  subject:
    topic: Machine Learning
  subject:
    topic: Imagerie pour le diagnostic
    topic: Techniques numériques
  subject:
    topic: Intelligence artificielle
    topic: Applications en médecine
  subject:
    topic: HEALTH & FITNESS
    topic: Diseases
    topic: General
  subject:
    topic: MEDICAL
    topic: Clinical Medicine
  subject:
    topic: MEDICAL
    topic: Diseases
  subject:
    topic: MEDICAL
    topic: Evidence-Based Medicine
  subject:
    topic: MEDICAL
    topic: Internal Medicine
  subject:
    topic: Artificial intelligence
    topic: Medical applications
  subject:
    topic: Diagnostic imaging
    topic: Digital techniques
  classification: RC78.7.D53
  classification: 616.07/54
  classification: 2016 I-614
  classification: WN 26.5
  related item:
    title info:
      title: Elsevier and MICCAI Society book series
  identifier: 9780128041147
  identifier: 0128041145
  identifier: 9780128040768
  identifier: 0128040769
  identifier: 2016936211
  identifier: 947427 MIL
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: IDEBK
    record creation date: 160819
    record change date: 20260407210835.9
    record identifier: in00005760933
    language of cataloging:
      language term: eng