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"Ideal for study, review, or self-assessment, 'The Essential Physics of Medical Imaging Study Guide' is an easy-to-use, image-rich resource for learning and retaining core information in the physics of medical imaging, radiation protection, and radiation biology. Based on 'The Essential Physics of Medical Imaging, 4th Edition' textbook (widely regarded as the cornerstone text in the field), the Study Guide can be used as a supplement to the textbook or as a stand-alone, concise review tool to focus on the most important aspects of the field. The Study Guide includes numerous vibrant full-color charts, graphs, and superb illustrations that reinforce central concepts. It's a must-have resource for medical imaging professionals, teachers, and students in medical physics and biomedical engineering, especially radiology residents preparing for certification exams. The Study Guide is perfect for anyone who needs a reliable, up-to-date review of the physics behind current medical imaging modalities and medical informatics, key concepts in radiation protection, regulatory requirements, dose optimization, and an up-to-date review of the biological effects of radiation exposure. The Study Guide's 21 chapter mirror the topics in the 4th edition textbook and contain key features organized in three sections for helpful study and review. Section 1 : Distills key concepts for each chapter in the 4th edition textbook in concise sentence and outline format. Icons indicating the corresponding page numbers for figures, tables, and equations make it easy to reference additional information on any topic in the 4th edition textbook. Section 2 : Includes more then 1,000 image-rich questions and explanatory answers with reference to page numbers in the 4th edition textbook for further detail. Section 3 : Consolidates Key Equations and Symbols for each chapter with pages numbers from the main text where the equation is introduced and discussed."--taken from back cover.

Diagnostic imaging.

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This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed. -- Edited summary from book.

Diagnostic imaging --- Data processing.

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Three-dimensional imaging.

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Three-dimensional imaging. --- 3-D imaging --- 3D imaging --- Three-dimensional imaging systems --- Three-dimensional imaging techniques --- Three-dimensional visualization --- Visualization, Three-dimensional --- Imaging systems

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Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks’ fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks. This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions.

Machine learning --- Diagnostic imaging --- Medical informatics --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Medicine --- Clinical imaging --- Imaging, Diagnostic --- Medical diagnostic imaging --- Medical imaging --- Noninvasive medical imaging --- Diagnosis, Noninvasive --- Imaging systems in medicine --- Learning, Machine --- Artificial intelligence --- Machine theory --- Data processing --- Diagnostic imaging. --- Data processing. --- Machine Learning. --- Artificial Intelligence --- Deep Learning --- Diagnostic Imaging --- Image Interpretation, Computer-Assisted --- methods