Deep Learning for Medical Applications with Unique Data

Deep Learning for Medical Applications with Unique Data is a book that discusses the latest deep learning-based medical applications using unique data gathered in real cases. It provides examples of how deep learning can be used in various problem areas and frameworks, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The book also includes negative findings obtained by deep learning techniques and discusses hybrid solutions with deep learning support, disease diagnosis, patient care and treatment, genomics research, and robotics and autonomous systems.

Format: Paperback / softback
Length: 256 pages
Publication date: 17 February 2022
Publisher: Elsevier Science Publishing Co Inc

Deep Learning for Medical Applications with Unique Data is a comprehensive guide that delves into the latest advancements in medical applications leveraging deep learning techniques. This book serves as a valuable resource for readers seeking to understand how deep learning can be applied in real-world medical scenarios, where only unique data gathered from actual cases is utilized.

The book offers a comprehensive overview of various problem areas and frameworks where deep learning can be effectively employed, encompassing clinical and research settings. It provides practical examples of how deep learning can be used in medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. Furthermore, the volume explores both positive findings and negative outcomes obtained through deep learning techniques, including the utilization of newly developed deep learning techniques that are rarely reported in the existing literature.

One of the key distinguishing features of this book is its focus on real-world cases. It excludes research works that rely on readily available data sets and instead includes only unique data usage to provide a more accurate understanding of the state of deep learning in real-world scenarios. Additionally, the book incorporates feedback and user experiences from physicians and medical staff who have applied deep learning-based solutions, offering valuable insights into their practical applications.

The book also presents other applications of deep learning in medical fields, such as hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems. Each application is thoroughly discussed, providing readers with a comprehensive understanding of the potential benefits and challenges associated with deep learning in medical applications.

In conclusion, Deep Learning for Medical Applications with Unique Data is a must-read for professionals, researchers, and students interested in leveraging deep learning for medical purposes. It offers a comprehensive and up-to-date overview of the latest developments in this field, providing practical insights and examples that can be applied in real-world medical scenarios. By focusing on unique data and incorporating feedback from physicians and medical staff, this book serves as a valuable resource for advancing the field of deep learning in medical applications.

Weight: 532g
Dimension: 190 x 232 x 21 (mm)
ISBN-13: 9780128241455