Academic Press Deep Learning for Medical Applications with Unique Data

Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include 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. Review Examines the concepts and technical methods of deep learning for a wide variety of clinical and research applications using unique data. From the Back Cover <p><b><i><i>Deep Learning for Medical Applications with Unique Data </i></i></b>informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include 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.</p> About the Author Dr. Deepak Gupta received a B.Tech. degree in 2006 from the Guru Gobind Singh Indraprastha University, Delhi, India. He received an M.E. degree in 2010 from Delhi Technological University, India, and a PhD in 2017 from Dr. APJ Abdul Kalam Technical University, Lucknow, India. He completed a post-doc degree at the National Institute of Telecommunications (Inatel), Brazil, in 2018. He has co-authored more than 155 journal articles, including 110 SCI papers and 45 conference articles. He has authored or edited 54 books published by IEEE-Wiley, Elsevier, Springer, Wiley, CRC Press, De Gruyter, and Katsons. He holds four patents in India. He is a convener of ICICC, ICDAM, and the DoSCI Springer conferences series. Currently, he is an associate editor of 'Expert Systems' (Wiley) and 'Intelligent Decision Technologies' (IOS Press). He is the recipient of the 2021 IEEE System Council Best Paper Award. He has been featured in the list of top 2% scientist/researcher in the world. He is working toward promoting start-ups and also serving as a consultant. Moreover, he is a series editor of 'Elsevier Biomedical Engineering' (Academic Press), 'Intelligent Biomedical Data Analysis' (De Gruyter), and 'Explaina