Machine learning and medical imaging
Wu, Guorong
Machine learning and medical imaging created by Guorong Wu, Dinggang Shen, Mert R. Sabuncu - London Academic Press 2016 - 487 pages illustrations - The Elsevier and MICCAI society book series .
Includes bibliographical references and index
This book presents state-of- the-art of 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 their 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.
Key Features:
Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems
Covers an array of medical imaging applications from computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics
Self-contained chapters with a thorough literature review
Assesses the development of future machine learning techniques and the further application of existing techniqu
9780128040768
Diagnostic imaging--Digital techniques
Artificial intelligence--Medical approach
Computer science
Medical informatics
Data mining
Machine learning and medical imaging created by Guorong Wu, Dinggang Shen, Mert R. Sabuncu - London Academic Press 2016 - 487 pages illustrations - The Elsevier and MICCAI society book series .
Includes bibliographical references and index
This book presents state-of- the-art of 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 their 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.
Key Features:
Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems
Covers an array of medical imaging applications from computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics
Self-contained chapters with a thorough literature review
Assesses the development of future machine learning techniques and the further application of existing techniqu
9780128040768
Diagnostic imaging--Digital techniques
Artificial intelligence--Medical approach
Computer science
Medical informatics
Data mining