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 in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs.
The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.
1. 2.1 Introduction - p. 59 2. 2.2 Mathematical Foundations - p. 33 3. 2.3 Applications - p. 54 4. 2.4 Conclusion and Future Directions - p. 57 5. Appendix A Reproducing Kernel Hilbert Spaces - p. 58 6. Appendix B Restricted Maximum Likelihood Estimation - p. 62 7. Chapter 3: Deep learning of brain images and its application to multiple sclerosis - p. 68 8. Chapter 4: Machine learning and its application in microscopic image analysis - p. 96 9. Chapter 5: Sparse models for imaging genetics - p. 127 10. Chapter 6: Dictionary learning for medical image denoising, reconstruction, and segmentation - p. 151 11. Chapter 7: Advanced sparsity techniques in magnetic resonance imaging - p. 180 12. Chapter 8: Hashing-based large-scale medical image retrieval for computer-aided diagnosis - p. 236 13. Part 2: Successful Applications in Medical Imaging - p. 255 14. Chapter 9: Multitemplate-based multiview learning for Alzheimer’s disease diagnosis - p. 255 15. Chapter 10: Machine learning as a means toward precision diagnostics and prognostics - p. 297 16. Chapter 11: Learning and predicting respiratory motion from 4D CT lung images - p. 334 17. Chapter 12: Learning pathological deviations from a normal pattern of myocardial motion: Added value for CRT studies? - p. 363 18. Chapter 13: From point to surface: Hierarchical parsing of human anatomy in medical images using machine learning technologies - p. 382 19. Chapter 14: Machine learning in brain imaging genomics - p. 410 20. Chapter 15: Holistic atlases of functional networks and interactions (HAFNI) - p. 434 21. Chapter 16: Neuronal network architecture and temporal lobe epilepsy: A connectome-based and machine learning study - p. 454 22. Index - p. 477
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 in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs.
The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.
1. 2.1 Introduction - p. 59 2. 2.2 Mathematical Foundations - p. 33 3. 2.3 Applications - p. 54 4. 2.4 Conclusion and Future Directions - p. 57 5. Appendix A Reproducing Kernel Hilbert Spaces - p. 58 6. Appendix B Restricted Maximum Likelihood Estimation - p. 62 7. Chapter 3: Deep learning of brain images and its application to multiple sclerosis - p. 68 8. Chapter 4: Machine learning and its application in microscopic image analysis - p. 96 9. Chapter 5: Sparse models for imaging genetics - p. 127 10. Chapter 6: Dictionary learning for medical image denoising, reconstruction, and segmentation - p. 151 11. Chapter 7: Advanced sparsity techniques in magnetic resonance imaging - p. 180 12. Chapter 8: Hashing-based large-scale medical image retrieval for computer-aided diagnosis - p. 236 13. Part 2: Successful Applications in Medical Imaging - p. 255 14. Chapter 9: Multitemplate-based multiview learning for Alzheimer’s disease diagnosis - p. 255 15. Chapter 10: Machine learning as a means toward precision diagnostics and prognostics - p. 297 16. Chapter 11: Learning and predicting respiratory motion from 4D CT lung images - p. 334 17. Chapter 12: Learning pathological deviations from a normal pattern of myocardial motion: Added value for CRT studies? - p. 363 18. Chapter 13: From point to surface: Hierarchical parsing of human anatomy in medical images using machine learning technologies - p. 382 19. Chapter 14: Machine learning in brain imaging genomics - p. 410 20. Chapter 15: Holistic atlases of functional networks and interactions (HAFNI) - p. 434 21. Chapter 16: Neuronal network architecture and temporal lobe epilepsy: A connectome-based and machine learning study - p. 454 22. Index - p. 477
