Machine Learning and Medical Imaging (eBook, ePUB)

Redaktion: Wu, Guorong; Sabuncu, Mert; Shen, Dinggang

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 512
• Erscheinungstermin: 11. August 2016
• Englisch
• ISBN-13: 9780128041147
• Artikelnr.: 45681183

Autorenporträt

Guorong Wu is an Assistant Professor of Radiology and Biomedical Research Imaging Center (BRIC) in the University of North Carolina at Chapel Hill. Dr. Wu received his PhD degree from the Department of Computer Science in Shanghai Jiao Tong University in 2007. After graduation, he worked for Pixelworks and joined University of North Carolina at Chapel Hill in 2009. Dr. Wu's research aims to develop computational tools for biomedical imaging analysis and computer assisted diagnosis. He is interested in medical image processing, machine learning and pattern recognition. He has published more than 100 papers in the international journals and conferences. Dr. Wu is actively in the development of medical image processing software to facilitate the scientific research on neuroscience and radiology therapy.

Inhaltsangabe

Part 1: Cutting-Edge Machine Learning Techniques in Medical Imaging

Chapter 1: Functional connectivity parcellation of the human brain

Chapter 2: Kernel machine regression in neuroimaging genetics

Chapter 3: Deep learning of brain images and its application to multiple sclerosis

Chapter 4: Machine learning and its application in microscopic image analysis

Chapter 5: Sparse models for imaging genetics

Chapter 6: Dictionary learning for medical image denoising, reconstruction, and segmentation

Chapter 7: Advanced sparsity techniques in magnetic resonance imaging

Chapter 8: Hashing-based large-scale medical image retrieval for computer-aided diagnosis

Part 2: Successful Applications in Medical Imaging

Chapter 9: Multitemplate-based multiview learning for Alzheimer's disease diagnosis

Chapter 10: Machine learning as a means toward precision diagnostics and prognostics

Chapter 11: Learning and predicting respiratory motion from 4D CT lung images

Chapter 12: Learning pathological deviations from a normal pattern of myocardial motion: Added value for CRT studies?

Chapter 13: From point to surface: Hierarchical parsing of human anatomy in medical images using machine learning technologies

Chapter 14: Machine learning in brain imaging genomics

Chapter 15: Holistic atlases of functional networks and interactions (HAFNI)

Chapter 16: Neuronal network architecture and temporal lobe epilepsy: A connectome-based and machine learning study