Machine learning and medical imaging / Guorong Wu, Dinggang Shen and Mert R. Sabuncu.

Includes bibliographical references and index.

Part 1: Cutting-Edge Machine Learning Techniques in Medical Imaging --
Functional Connectivity Parcellation of the Human Brain --
Kernel Machine Regression in Neuroimaging Genetics --
Deep Learning of Brain Images and Its Application to Multiple Sclerosis --
Machine Learning and Its Application in Microscropic Image Analysis --
Sparse Models for Imaging Genetics --
Dictionary Learning for Medical Image Denoising, Reconstruction, and Segmentation --
Advanced Sparsity Techniques in Magnetic Resonance Imaging --
Hashing-Based Large-Scale Medical Image Retrieval for Computer-Aided Diagnosis --
Part 2: Successful Applications in Medical Imaging --
Multitemplate-Based Multiview Learning for Alzheimer's Disease Diagnosis --
Machine Learning as a Means Toward Precision Diagnostics and Prognostics --
Learning and Predicting Respiratory Motion From 4D CT Lung Images --
Learning Pathological Deviations From a Normal Pattern of Myocardial Motion: Added Value for CRT Studies? --
From Point to Surface: Hierarchical Parsing of Human Anatomy in Medical Images Using Machine Learning Technologies --
Machine Learning in Brain Imaging Genomics --
Holistic Atlases of Functional Networks and Interactions (HAFNI) --
Neuronal Network Architecture and Temporal Lobe Epilepsy: A Connectome-Based and Machine Learning Study.

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.