Deep Learning and Medical Applications 1st Edition by Jin Keun Seo ISBN 9819918383 9789819918386

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ISBN 10: 9819918383

ISBN 13: 978-9819918386

Author:  Jin Keun Seo 

Deep Learning and Medical Applications 1st Edition : Over the past 40 years, diagnostic medical imaging has undergone remarkable advancements in CT, MRI, and ultrasound technology. Today, the field is experiencing a major paradigm shift, thanks to significant and rapid progress in deep learning techniques. As a result, numerous innovative AI-based programs have been developed to improve image quality and enhance clinical workflows, leading to more efficient and accurate diagnoses.
AI advancements of medical imaging not only address existing unsolved problems but also present new and complex challenges. Solutions to these challenges can improve image quality and reveal new information currently obscured by noise, artifacts, or other signals. Holistic insight is the key to solving these challenges. Such insight may lead to a creative solution only when it is based on a thorough understanding of existing methods and unmet demands.
This book focuses on advanced topics in medical imagingmodalities, including CT and ultrasound, with the aim of providing practical applications in the healthcare industry. It strikes a balance between mathematical theory, numerical practice, and clinical applications, offering comprehensive coverage from basic to advanced levels of mathematical theories, deep learning techniques, and algorithm implementation details. Moreover, it provides in-depth insights into the latest advancements in dental cone-beam CT, fetal ultrasound, and bioimpedance, making it an essential resource for professionals seeking to stay up-to-date with the latest developments in the field of medical imaging.

Deep Learning and Medical Applications 1st Edition Table of contents:

Chapter 1. Nonlinear Representation and Dimensionality Reduction
Chapter 2. Deep Learning Techniques for Medical Image Segmentation and Object Recognition
Chapter 3. Deep Learning for Dental Cone-Beam Computed Tomography
Chapter 4. Artificial Intelligence for Digital Dentistry
Chapter 5. Artificial Intelligence for Fetal Ultrasound
Chapter 6. Electrical Impedance Imaging
Chapter 7. Deep Learning for Ill Posed Inverse Problems in Medical Imaging

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