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Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making

In recent years, AI/ML tools have become more prevalent in the fields of medical imaging and imaging informatics, where systems are already outperforming physicians in a range of domains, such as in the classification of retinal fundus images in ophthalmology, chest X-rays in radiology, and skin cancer detection in dermatology, among many others. It has recently emerged as one of the fastest growing research areas given the evolution of techniques in radiology, molecular imaging, anatomical imaging, and functional imaging for detection, segmentation, diagnosis, annotation, summarization, and prediction. The ongoing innovations in this exciting and promising field play a powerful role in influencing the lives of millions through health, safety, education, and other opportunities intended to be shared across all segments of society. To achieve further progress, this Special Issue (SI) invited both research and review-type manuscripts to showcase ongoing research progress and development based on applications of AI/ML (especially DL techniques) in medical imaging to influence human health and healthcare systems in the diagnostic decision-making process. The SI published fourteen articles after a rigorous peer-review process across the spectrum of medical imaging modalities and the diversity of specialties depending on imaging techniques from radiology, dermatology, pathology, colonoscopy, endoscopy, etc.

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Series Title
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Call Number
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Publisher
www.mdpi.com/books : MDPI - Multidisciplinary Digital Publishing Institute.,
Collation
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Language
English
ISBN/ISSN
9783036581286
Classification
NONE
Content Type
text
Media Type
computer
Carrier Type
online resource
Edition
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Subject(s)
Nursing
Medicine and Nursing
Specific Detail Info
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Statement of Responsibility
Other Information
Cataloger
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Source
https://directory.doabooks.org/handle/20.500.12854/112457
Validator
Suwardi
Digital Object Identifier (DOI)
10.3390/books978-3-0365-8129-3
Journal Volume
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Journal Issue
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Subtitle
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Parallel Title
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