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This timely text/reference presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Topics and features: Highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing Discusses the insightful research experience and views of Dr. Ronald M. Summers in medical imaging-based computer-aided diagnosis and its interaction with deep learning Presents a comprehensive review of the latest research and literature on deep learning for medical image analysis Describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging Examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging Introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database for automated image interpretation This pioneering volume will prove invaluable to researchers and graduate students wishing to employ deep neural network models and representations for medical image analysis and medical imaging applications. Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA. Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA. Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia. Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

Computer science. --- Radiology. --- Artificial intelligence. --- Image processing. --- Neural networks (Computer science). --- Computer Science. --- Image Processing and Computer Vision. --- Artificial Intelligence (incl. Robotics). --- Mathematical Models of Cognitive Processes and Neural Networks. --- Imaging / Radiology. --- Neural networks (Computer science) --- Diagnostic imaging --- Data processing. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Clinical imaging --- Imaging, Diagnostic --- Medical diagnostic imaging --- Medical imaging --- Noninvasive medical imaging --- Diagnosis, Noninvasive --- Imaging systems in medicine --- Computer vision. --- Radiology, Medical. --- Artificial Intelligence. --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Clinical radiology --- Radiology, Medical --- Radiology (Medicine) --- Medical physics --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Optical data processing. --- Neural networks (Computer science) . --- Radiological physics --- Physics --- Radiation --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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This timely text/reference presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Topics and features: Highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing Discusses the insightful research experience and views of Dr. Ronald M. Summers in medical imaging-based computer-aided diagnosis and its interaction with deep learning Presents a comprehensive review of the latest research and literature on deep learning for medical image analysis Describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging Examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging Introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database for automated image interpretation This pioneering volume will prove invaluable to researchers and graduate students wishing to employ deep neural network models and representations for medical image analysis and medical imaging applications. Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA. Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA. Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia. Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.

Mathematics --- Physical methods for diagnosis --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- neuronale netwerken --- beeldverwerking --- deep learning --- pneumologie --- radiologie --- medische beeldvorming --- wiskunde --- KI (kunstmatige intelligentie)

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This book constitutes the thoroughly refereed workshop proceedings of the Second International Workshop on Medical Computer Vision, MCV 2012, held in Nice, France, October 2012 in conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012. The 24 papers have been selected out of 42 submissions. At MCV 2012, 12 papers were presented as a poster and 12 as a poster together with a plenary talk. The book also features four selected papers which were presented at the previous CVPR Medical Computer Vision workshop held in conjunction with the International Conference on Computer Vision and Pattern Recognition on June 21 2012 in Providence, Rhode Island, USA. The papers explore the use of modern computer vision technology in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies, as well as 3D reconstruction and biophysical model personalization.

Computer science. --- Medical records --- Computer vision. --- Optical pattern recognition. --- Computer Science. --- Image Processing and Computer Vision. --- Health Informatics. --- Pattern Recognition. --- Computer Applications. --- Computer vision in medicine --- Diagnostic imaging --- Engineering & Applied Sciences --- Applied Physics --- Data processing. --- Data processing --- Machine vision --- Vision, Computer --- EHR systems --- EHR technology --- EHRs (Electronic health records) --- Electronic health records --- Electronic medical records --- EMR systems --- EMRs (Electronic medical records) --- Informatics --- Health informatics. --- Image processing. --- Pattern recognition. --- Application software. --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Science --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Information storage and retrieval systems --- Medical care --- Optical data processing. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Medicine --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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This book constitutes the thoroughly refereed workshop proceedings of the Second International Workshop on Medical Computer Vision, MCV 2012, held in Nice, France, October 2012 in conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012. The 24 papers have been selected out of 42 submissions. At MCV 2012, 12 papers were presented as a poster and 12 as a poster together with a plenary talk. The book also features four selected papers which were presented at the previous CVPR Medical Computer Vision workshop held in conjunction with the International Conference on Computer Vision and Pattern Recognition on June 21 2012 in Providence, Rhode Island, USA. The papers explore the use of modern computer vision technology in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies, as well as 3D reconstruction and biophysical model personalization.

Mathematical statistics --- Human medicine --- Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- patroonherkenning --- beeldverwerking --- factoranalyse --- computers --- medische informatica --- computerkunde --- optica