یادگیری ماشین و تصویرسازی پزشکی (مجموعه کتاب‌های جامعه MICCAI)

__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. * Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems * Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics * Features self-contained chapters with a thorough literature review * Assesses the development of future machine learning techniques and the further application of existing techniques Content: Front Matter,Copyright,Contributors,Editor Biographies,Preface,AcknowledgmentsEntitled to full textPart 1: Cutting-Edge Machine Learning Techniques in Medical ImagingChapter 1 - Functional connectivity parcellation of the human brain, Pages 3-29, A. Schaefer, R. Kong, B.T.Thomas Yeo Chapter 2 - Kernel machine regression in neuroimaging genetics, Pages 31-68, T. Ge, J.W. Smoller, M.R. Sabuncu Chapter 3 - Deep learning of brain images and its application to multiple sclerosis, Pages 69-96, T. Brosch, Y. Yoo, L.Y.W. Tang, R. Tam Chapter 4 - Machine learning and its application in microscopic image analysis, Pages 97-127, F. Xing, L. Yang Chapter 5 - Sparse models for imaging genetics, Pages 129-151, J. Wang, T. Yang, P. Thompson, J. Ye Chapter 6 - Dictionary learning for medical image denoising, reconstruction, and segmentation, Pages 153-181, T. Tong, J. Caballero, K. Bhatia, D. Rueckert Chapter 7 - Advanced sparsity techniques in magnetic resonance imaging, Pages 183-236, J. Huang, Y. Li Chapter 8 - Hashing-based large-scale medical image retrieval for computer-aided diagnosis, Pages 237-255, X. Zhang, S. Zhang Chapter 9 - Multitemplate-based multiview learning for Alzheimer’s disease diagnosis, Pages 259-297, M. Liu, R. Min, Y. Gao, D. Zhang, D. Shen Chapter 10 - Machine learning as a means toward precision diagnostics and prognostics, Pages 299-334, A. Sotiras, B. Gaonkar, H. Eavani, N. Honnorat, E. Varol, A. Dong, C. Davatzikos Chapter 11 - Learning and predicting respiratory motion from 4D CT lung images, Pages 335-363, T. He, Z. Xue Chapter 12 - Learning pathological deviations from a normal pattern of myocardial motion: Added value for CRT studies?, Pages 365-382, N. Duchateau, G. Piella, A. Frangi, M. De Craene Chapter 13 - From point to surface: Hierarchical parsing of human anatomy in medical images using machine learning technologies, Pages 383-410, Y. Zhan, M. Dewan, S. Zhang, Z. Peng, B. Jian, X.S. Zhou Chapter 14 - Machine learning in brain imaging genomics, Pages 411-434, J. Yan, L. Du, X. Yao, L. Shen Chapter 15 - Holistic atlases of functional networks and interactions (HAFNI), Pages 435-454, X. Jiang, D. Zhu, T. Liu Chapter 16 - Neuronal network architecture and temporal lobe epilepsy: A connectome-based and machine learning study, Pages 455-476, B.C. Munsell, G. Wu, S. Keller, J. Fridriksson, B. Weber, M. Stoner, D. Shen, L. Bonilha Index, Pages 477-487

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.

• Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems
• Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics
• Features self-contained chapters with a thorough literature review
• Assesses the development of future machine learning techniques and the further application of existing techniques

Featuring self-contained chapters with a thorough literature review, this thorough reference demonstrates the application of cutting-edge machine learning techniques to medical imaging problems. -- Edited summary from book