| Description |
1 online resource (514 pages). |
|
text txt rdacontent |
|
computer c rdamedia |
|
online resource cr rdacarrier |
| Series |
The Elsevier and MICCAI society book series |
|
Elsevier and MICCAI Society book series.
|
| Note |
Print version record. |
| 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. |
|
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. |
|
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. |
|
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. |
| Bibliography |
Includes bibliographical references and index. |
| Subject |
Diagnostic imaging -- Digital techniques.
|
|
Artificial intelligence -- Medical applications.
|
|
Image Processing, Computer-Assisted |
|
Machine Learning |
|
Imagerie pour le diagnostic -- Techniques numériques.
|
|
Intelligence artificielle en médecine.
|
|
HEALTH & FITNESS -- Diseases -- General.
|
|
MEDICAL -- Clinical Medicine.
|
|
MEDICAL -- Diseases.
|
|
MEDICAL -- Evidence-Based Medicine.
|
|
MEDICAL -- Internal Medicine.
|
|
Artificial intelligence -- Medical applications
|
|
Diagnostic imaging -- Digital techniques
|
| Added Author |
Shen, Dinggang.
|
|
Sabuncu, Mert Rory, 1979-
|
| Other Form: |
Print version : 9780128040768 |
| ISBN |
9780128041147 (electronic bk.) |
|
0128041145 (electronic bk.) |
|
9780128040768 |
|
0128040769 |
| Standard No. |
CHBIS 010705852 |
|
CHVBK 368888916 |
|
DEBSZ 482473045 |
|
GBVCP 879398892 |
|
AU@ 000061154195 |
|
CHVBK 519314646 |
|
CHNEW 001013753 |
|
UKMGB 017969424 |
