Machine learning and medical imaging
Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs...
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| Main Authors | , , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Amsterdam
Academic Press
2016
Elsevier Science & Technology Elsevier Science and Technology Books, Inc |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780128040768 0128040769 9780128041147 0128041145 |
Cover
Table of Contents:
- 4.3.2 Sparsity-Based Dictionary Learning -- 4.3.2.1 CNN-based shape initialization -- 4.3.2.2 Sparsity-based shape modeling -- 4.3.2.3 Local repulsive deformable model -- 4.3.2.4 Experimental results -- 4.4 Summary -- References -- Chapter 5: Sparse models for imaging genetics -- 5.1 Introduction -- 5.2 Basic Sparse Models -- 5.3 Structured Sparse Models -- 5.3.1 Group Lasso and Sparse Group Lasso -- 5.3.2 Overlapping Group Lasso and Tree Lasso -- 5.3.3 Fused Lasso and Graph Lasso -- 5.4 Optimization Methods -- 5.4.1 Proximal Gradient Descent -- 5.4.2 Accelerated Gradient Method -- 5.5 Screening -- 5.5.1 Screening for Lasso -- 5.5.1.1 Background -- 5.5.1.2 Enhanced DPP (EDPP) screening rules -- 5.5.1.3 Applications of EDPP to imaging genetics -- 5.5.2 Screening Methods for Other Sparse Models -- 5.6 Conclusions -- References -- Chapter 6: Dictionary learning for medical image denoising, reconstruction, and segmentation -- 6.1 Introduction -- 6.1.1 The Convenience of Orthogonal Transforms -- 6.1.2 The Flexibility of Overcomplete Dictionaries -- 6.2 Sparse Coding and Dictionary Learning -- 6.2.1 Sparse Coding -- 6.2.2 Dictionary Learning Problem -- 6.2.3 K-SVD Dictionary Learning -- 6.2.4 Online Dictionary Learning -- 6.3 Patch-Based Dictionary Sparse Coding -- 6.3.1 Overcompleteness -- 6.3.2 Redundancy -- 6.3.3 Adaptability -- 6.4 Application of Dictionary Learning in Medical Imaging -- 6.4.1 Denoising -- 6.4.2 Reconstruction -- 6.4.3 Super-Resolution -- 6.4.4 Segmentation -- 6.5 Future Directions -- 6.6 Conclusion -- References -- Glossary -- Chapter 7: Advanced sparsity techniques in magnetic resonance imaging -- 7.1 Introduction -- 7.2 Standard Sparsity in CS-MRI -- 7.2.1 Model and Algorithm -- 7.2.1.1 Related acceleration algorithm -- 7.2.1.2 CSA and FCSA -- Sketch Proof of Theorem 7.2 -- End of Proof -- 7.2.2 Evaluation
- 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 Methods -- 2.2.2 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 Matrix -- Appendix 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
- Chapter 9: Multitemplate-based multiview learning for Alzheimer's disease diagnosis -- 9.1 Background -- 9.2 Multiview Feature Representation With MR Imaging -- 9.2.1 Preprocessing -- 9.2.2 Template Selection -- 9.2.3 Registration and Quantification -- 9.2.4 Feature Extraction -- 9.2.4.1 Watershed segmentation -- 9.2.4.2 Regional feature aggregation -- 9.2.4.3 Anatomical analysis -- 9.3 Multiview Learning Methods for AD Diagnosis -- 9.3.1 Feature Filtering-Based Multiview Learning -- 9.3.2 Maximum-Margin-Based Representation Learning -- 9.3.3 View-Centralized Multiview Learning -- Ensemble classification -- 9.3.4 Relationship-Induced Multiview Learning -- 9.4 Experiments -- 9.4.1 Subjects -- 9.4.2 Experimental Settings -- 9.4.3 Results of Feature Filtering-Based Method for AD/MCI Diagnosis -- 9.4.4 Results of Maximum-Margin-Based Learning for AD/MCI Diagnosis -- 9.4.5 Results of View-Centralized Learning for AD/MCI Diagnosis -- 9.4.6 Results of Relationship-Induced Learning for AD/MCI Diagnosis -- 9.5 Summary -- References -- Chapter 10: Machine learning as a means toward precision diagnostics and prognostics -- 10.1 Introduction -- 10.2 Dimensionality Reduction -- 10.2.1 Dimensionality Reduction Through Spatial Grouping -- 10.2.2 Spatial Grouping of Structural MRI -- 10.2.2.1 Spatial grouping of rs-fMRI -- 10.2.3 Statistically Driven Dimensionality Reduction -- 10.2.3.1 Statistically driven dimensionality reduction of structural MRI -- 10.2.3.2 Statistically driven dimensionality reduction of functional MRI -- 10.3 Model Interpretation: From Classification to Statistical Significance Maps -- 10.4 Heterogeneity -- 10.4.1 Generative Framework -- 10.4.2 Discriminative Framework -- 10.4.3 Generative Discriminative Framework -- 10.5 Applications -- 10.5.1 Individualized Diagnostic Indices Using MRI -- 10.5.2 MRI-Based Diagnosis of AD: The SPARE-AD
- Alternative unit types -- 3.1.1.3 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 dataset -- 3.2.3.4 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 -- 4.2.1 Support Vector Machine -- 4.2.1.1 Image preprocessing -- 4.2.1.2 Robust ellipse fitting -- 4.2.1.3 SVM-based ellipse refinement -- Group 1 -- Group 2 -- Group 3 -- Group 4 -- Group 5 -- Group 6 -- 4.2.1.4 Inner geodesic distance-based clustering -- 4.2.2 Deep Convolutional Neural Network -- 4.2.2.1 CNN-based structured regression -- 4.2.2.2 CNN architecture -- 4.2.2.3 Structured prediction fusion and cell localization -- 4.2.2.4 Experimental results -- 4.3 Segmentation -- 4.3.1 Random Forests -- 4.3.1.1 Structured edge detection -- 4.3.1.2 Hierarchical image segmentation -- 4.3.1.3 Experimental results
- 7.2.2.1 Experimental setup -- 7.2.2.2 Visual comparisons -- 7.2.2.3 CPU time and SNRs -- 7.2.2.4 Sample ratios -- 7.2.3 Summary -- 7.3 Group Sparsity in Multicontrast MRI -- 7.3.1 Model and Algorithm -- 7.3.1.1 Proposed fast multicontrast reconstruction -- 7.3.2 Evaluation -- 7.3.2.1 SRI24 multichannel brain atlas data -- 7.3.2.2 Complex-valued Shepp-Logan phantoms data -- 7.3.2.3 Complex-valued turbo spin echo slices with early and late TEs data -- 7.3.2.4 The benefit of group sparsity on both wavelet and gradient domains -- 7.3.2.5 Results on SRI24 multichannel brain atlas data -- 7.3.2.6 Results on Complex-valued Shepp-Logan phantoms data -- 7.3.2.7 Results on Complex-valued turbo spin echo slices with early and late TEs -- 7.3.2.8 Discussion -- 7.3.3 Summary -- 7.4 Tree Sparsity in Accelerated MRI -- 7.4.1 Model and Algorithm -- 7.4.1.1 Unconstrained tree-based MRI -- 7.4.1.2 Constrained tree-based MRI -- 7.4.2 Evaluation -- 7.4.2.1 Experimental setup -- 7.4.2.2 Group configuration for tree sparsity -- 7.4.2.3 Visual comparisons -- 7.4.2.4 SNRs and CPU time -- 7.4.2.5 Sampling ratios -- 7.4.2.6 Complex-valued image with radial sampling mask -- 7.4.3 Summary -- 7.5 Forest Sparsity in Multichannel CS-MRI -- 7.5.1 Model and Algorithm -- 7.5.2 Evaluation -- 7.5.2.1 Multicontrast MRI -- 7.5.2.2 Parallel MRI -- 7.5.3 Summary -- 7.6 Conclusion -- References -- Chapter 8: Hashing-based large-scale medical image retrieval for computer-aided diagnosis -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Supervised Hashing for Large-Scale Retrieval -- 8.3.1 Overview of Scalable Image Retrieval Framework -- 8.3.2 Kernelized and Supervised Hashing -- 8.3.2.1 Hashing method -- 8.3.2.2 Kernelized hashing -- 8.3.2.3 Supervised hashing -- 8.4 Results -- 8.5 Discussion and Future Work -- References -- Part 2: Successful applications in medical imaging
- 10.5.3 Individualized Early Predictions