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Titlebook: Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging; Patrick Veit-Haibach,Ken Herrmann Book 2022 The Editor(s)

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期刊全称Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging
影响因子2023Patrick Veit-Haibach,Ken Herrmann
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发行地址No other book on this topic available.Specifically focused on nuclear medicine and hybrid imaging.Addresses multiple aspects of clinical nuclear medicine and hybrid imaging, not just the technological
图书封面Titlebook: Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging;  Patrick Veit-Haibach,Ken Herrmann Book 2022 The Editor(s)
影响因子.This book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic..A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice..As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists
Pindex Book 2022
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978-3-031-00121-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Introduction to Optimal Control,conditions. However, radiomics parameters are for example influenced by the type of acquisition and reconstruction, the image processing and even the software being used for feature extraction. This chapter will give a short overview over the impact of each step along the radiomic evaluation pipelin
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