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Artificial Intelligence in Medical Imaging

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Cover of 'Artificial Intelligence in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction: Game Changers in Radiology
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    Chapter 2 The Role of Medical Image Computing and Machine Learning in Healthcare
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    Chapter 3 A Deeper Understanding of Deep Learning
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    Chapter 4 Deep Learning and Machine Learning in Imaging: Basic Principles
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    Chapter 5 How to Develop Artificial Intelligence Applications
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    Chapter 6 A Standardised Approach for Preparing Imaging Data for Machine Learning Tasks in Radiology
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    Chapter 7 The Value of Structured Reporting for AI
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    Chapter 8 Artificial Intelligence in Medicine: Validation and Study Design
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    Chapter 9 Enterprise Imaging
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    Chapter 10 Imaging Biomarkers and Imaging Biobanks
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    Chapter 11 Applications of AI Beyond Image Interpretation
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    Chapter 12 Artificial Intelligence and Computer-Assisted Evaluation of Chest Pathology
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    Chapter 13 Cardiovascular Diseases
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    Chapter 14 Deep Learning in Breast Cancer Screening
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    Chapter 15 Neurological Diseases
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    Chapter 16 The Role of AI in Clinical Trials
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    Chapter 17 Quality and Curation of Medical Images and Data
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    Chapter 18 Does Future Society Need Legal Personhood for Robots and AI?
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    Chapter 19 The Role of an Artificial Intelligence Ecosystem in Radiology
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    Chapter 20 Advantages, Challenges, and Risks of Artificial Intelligence for Radiologists
Attention for Chapter 14: Deep Learning in Breast Cancer Screening
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Chapter title
Deep Learning in Breast Cancer Screening
Chapter number 14
Book title
Artificial Intelligence in Medical Imaging
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-319-94878-2_14
Book ISBNs
978-3-31-994877-5, 978-3-31-994878-2
Authors

Hugh Harvey, Andreas Heindl, Galvin Khara, Dimitrios Korkinof, Michael O’Neill, Joseph Yearsley, Edith Karpati, Tobias Rijken, Peter Kecskemethy, Gabor Forrai

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 44%
Researcher 4 25%
Other 3 19%
Student > Master 2 13%
Student > Doctoral Student 1 6%
Other 1 6%
Readers by discipline Count As %
Computer Science 7 44%
Medicine and Dentistry 4 25%
Engineering 3 19%
Agricultural and Biological Sciences 1 6%
Nursing and Health Professions 1 6%
Other 0 0%