Titlebook: Artificial Intelligence in Medical Imaging; Opportunities, Appli Erik R. Ranschaert,Sergey Morozov,Paul R. Algra Book 2019 Springer Nature

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Farm-Level Microsimulation Modellingom imaging is combined with other data such as the results from laboratory evaluations, genetic analysis, medication use and personal fitness trackers. Nevertheless, the process of bringing the results to physicians is nontrivial, and we also discuss our experience with deployment of developed algor

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tionsfor radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imagi978-3-319-94878-2

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Introduction: Game Changers in Radiology are creating a real hype around artificial intelligence for automated image analysis, hereby exerting external pressure on radiologists to reevaluate the value and future of their profession. Radiologists from their side seem to be rather reluctant to embrace and implement these new technological o

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A Deeper Understanding of Deep Learningcuss the power of contextual processing, study insights from the human visual system, and study in some detail how the different of a deep convolutional neural networks work. We do this with an engineering view, for radiologists, in an intuitive way.

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Deep Learning and Machine Learning in Imaging: Basic Principlesly on a class of algorithms known as deep learning. Prior machine learning methods are still useful and can provide a good understanding of machine learning fundamentals. Deep learning methods are still seeing rapid advances, but there are several basic components that are likely to be durable. This

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