Deep Learning and Medical Applications
Jin Keun Seo
āļĄāļī.āļĒ. 2023 · Mathematics in Industry āļŦāļāļąāļāļŠāļ·āļāđāļĨāđāļĄāļāļĩāđ 40 · Springer Nature
eBook
339
āļŦāļāđāļē
reportāļāļ°āđāļāļāđāļĨāļ°āļĢāļĩāļ§āļīāļ§āđāļĄāđāđāļāđāļĢāļąāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļĒāļ·āļāļĒāļąāļ Â āļāļđāļāđāļāļĄāļđāļĨāđāļāļīāđāļĄāđāļāļīāļĄ
āđāļāļĩāđāļĒāļ§āļāļąāļ eBook āđāļĨāđāļĄāļāļĩāđ
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.
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.
āđāļāļĩāđāļĒāļ§āļāļąāļāļāļđāđāđāļāđāļ
Professor Jin Keun Seo is a Professor of School of Mathematics and Computing (Computational Science and Engineering) at Yonsei University.
āđāļŦāđāļāļ°āđāļāļ eBook āļāļĩāđ
āđāļŠāļāļāļāļ§āļēāļĄāđāļŦāđāļāļāļāļāļāļļāļāđāļŦāđāđāļĢāļēāļĢāļąāļāļĢāļđāđ
āļāđāļāļĄāļđāļĨāđāļāļāļēāļĢāļāđāļēāļ
āļŠāļĄāļēāļĢāđāļāđāļāļāđāļĨāļ°āđāļāđāļāđāļĨāđāļ
āļāļīāļāļāļąāđāļāđāļāļ Google Play Books āļŠāļģāļŦāļĢāļąāļ Android āđāļĨāļ° iPad/iPhone āđāļāļāļāļ°āļāļīāļāļāđāđāļāļĒāļāļąāļāđāļāļĄāļąāļāļīāļāļąāļāļāļąāļāļāļĩāļāļāļāļāļļāļ āđāļĨāļ°āļāđāļ§āļĒāđāļŦāđāļāļļāļāļāđāļēāļāđāļāļāļāļāļāđāļĨāļāđāļŦāļĢāļ·āļāļāļāļāđāļĨāļāđāđāļāđāļāļļāļāļāļĩāđ
āđāļĨāđāļāļāđāļāļāđāļĨāļ°āļāļāļĄāļāļīāļ§āđāļāļāļĢāđ
āļāļļāļāļāļąāļāļŦāļāļąāļāļŠāļ·āļāđāļŠāļĩāļĒāļāļāļĩāđāļāļ·āđāļāļāļēāļ Google Play āđāļāļĒāđāļāđāđāļ§āđāļāđāļāļĢāļēāļ§āđāđāļāļāļĢāđāđāļāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāđāļāđ
eReader āđāļĨāļ°āļāļļāļāļāļĢāļāđāļāļ·āđāļāđ
āļŦāļēāļāļāđāļāļāļāļēāļĢāļāđāļēāļāļāļāļāļļāļāļāļĢāļāđ e-ink āđāļāđāļ Kobo eReader āļāļļāļāļāļ°āļāđāļāļāļāļēāļ§āļāđāđāļŦāļĨāļāđāļĨāļ°āđāļāļāđāļāļĨāđāđāļāļĒāļąāļāļāļļāļāļāļĢāļāđāļāļāļāļāļļāļ āđāļāļĢāļāļāļģāļāļēāļĄāļ§āļīāļāļĩāļāļēāļĢāļāļĒāđāļēāļāļĨāļ°āđāļāļĩāļĒāļāđāļāļĻāļđāļāļĒāđāļāđāļ§āļĒāđāļŦāļĨāļ·āļāđāļāļ·āđāļāđāļāļāđāļāļĨāđāđāļāļĒāļąāļ eReader āļāļĩāđāļĢāļāļāļĢāļąāļ
