Cargando…
Machine learning and medical imaging /
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press is an imprint of Elsevier,
2016.
|
| Colección: | Elsevier and MICCAI Society book series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.