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This LNCS workshop proceedings, ACCV 2018, contains carefully reviewed and selected papers from 11 workshops, each having different types or programs: Scene Understanding and Modelling (SUMO) Challenge, Learning and Inference Methods for High Performance Imaging (LIMHPI), Attention/Intention Understanding (AIU), Museum Exhibit Identification Challenge (Open MIC) for Domain Adaptation and Few-Shot Learning, RGB-D - Sensing and Understanding via Combined Colour and Depth, Dense 3D Reconstruction for Dynamic Scenes, AI Aesthetics in Art and Media (AIAM), Robust Reading (IWRR), Artificial Intelligence for Retinal Image Analysis (AIRIA), Combining Vision and Language, Advanced Machine Vision for Real-life and Industrially Relevant Applications (AMV).

Computer vision. --- Artificial intelligence. --- Computer network architectures. --- Computer hardware. --- Image Processing and Computer Vision. --- Artificial Intelligence. --- Computer Systems Organization and Communication Networks. --- Computer Hardware. --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- 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 --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Computer vision --- Computer graphics --- Optical data processing. --- Computer organization. --- Organization, Computer --- Electronic digital computers --- 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.

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 refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Computer science. --- Health informatics. --- Logic design. --- Artificial intelligence. --- Image processing. --- Bioinformatics. --- Computer Science. --- Image Processing and Computer Vision. --- Artificial Intelligence (incl. Robotics). --- Health Informatics. --- Computational Biology/Bioinformatics. --- Logic Design. --- Bio-informatics --- Biological informatics --- Pictorial data processing --- Picture processing --- Processing, Image --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Design, Logic --- Design of logic systems --- Informatics --- Clinical informatics --- Health informatics --- Medical information science --- Biology --- Information science --- Computational biology --- Systems biology --- Imaging systems --- Optical data processing --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Digital electronics --- Electronic circuit design --- Logic circuits --- Switching theory --- Science --- Medicine --- Data processing --- Computer vision. --- Medical records --- Artificial Intelligence. --- Data processing. --- EHR systems --- EHR technology --- EHRs (Electronic health records) --- Electronic health records --- Electronic medical records --- EMR systems --- EMRs (Electronic medical records) --- Information storage and retrieval systems --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Medical care --- Image analysis --- Analysis of images --- Image interpretation --- Photographs --- Forensic sciences --- Inspection --- Optical data processing. --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment