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This volume describes concurrent engineering developments that affect or are expected to influence future development of digital diagnostic imaging. It also covers current developments in Picture Archiving and Communications System (PACS) technology, with particular emphasis on integration of emerging imaging technologies into the hospital environment.
This volume constitutes the edited proceedings of an interdisciplinary symposium on Methods of Heuristics, which was held at the University of Bern, Switzerland, from September 15 to 19, 1980. In organizing the symposium, the editors of the present volume were able to invite specialists from psychology, computer science, and mathematics. From their own perspective they made contributions to the central questions of the conference: What are heuristics, the methods and rules guiding discovery and problem solving in a variety of different fields? How did they develop in individual human beings and in the history of science? Is it possible to arrive at a commonly accepted definition of heuristics as the field unifying all these efforts, and, if yes, what are its basic characteristics?
An examination of the bodily, situated aspects of data-visualization work, looking at visualization practices around the development of MRI technology. Our bodies are scanned, probed, imaged, sampled, and transformed into data by clinicians and technologists. In this book, Silvia Casini reveals the affective relations and materiality that turn data into image--and in so doing, gives bodies back to data. Opening the black box of MRI technology, Casini examines the bodily, situated aspects of visualization practices around the development of this technology. Reframing existing narratives of biomedical innovation, she emphasizes the important but often overlooked roles played by aesthetics, aff...
The Novartis Foundation Series is a popular collection of the proceedings from Novartis Foundation Symposia, in which groups of leading scientists from a range of topics across biology, chemistry and medicine assembled to present papers and discuss results. The Novartis Foundation, originally known as the Ciba Foundation, is well known to scientists and clinicians around the world.
A state-of-the-art review of key topics in medical image perception science and practice, including associated techniques, illustrations and examples. This second edition contains extensive updates and substantial new content. Written by key figures in the field, it covers a wide range of topics including signal detection, image interpretation and advanced image analysis (e.g. deep learning) techniques for interpretive and computational perception. It provides an overview of the key techniques of medical image perception and observer performance research, and includes examples and applications across clinical disciplines including radiology, pathology and oncology. A final chapter discusses the future prospects of medical image perception and assesses upcoming challenges and possibilities, enabling readers to identify new areas for research. Written for both newcomers to the field and experienced researchers and clinicians, this book provides a comprehensive reference for those interested in medical image perception as means to advance knowledge and improve human health.
Publishes papers reporting on research and development in optical science and engineering and the practical applications of known optical science, engineering, and technology.
Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems. - Explains design principles of deep learning techniques for MIC - Contains cutting-edge deep learning research on MIC - Covers a broad range of MIC tasks, including the classification, detection, segmentation, registration, reconstruction and synthesis of medical images
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Volume 2 addresses the methods in use or in development for enhancing the visual perception of digital medical images obtained by a wide variety of imaging modalities and for image analysis as an aid to detection and diagnosis. Softcover version of PM80.