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 is used. The editors provide examples of how Deep Learning can be used in different problem scopes 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 editors discuss not only positive findings but also negative findings 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 for better understanding 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. Introduces the concept of Deep Learning and demonstrates Deep Learning for a wide variety of medical applications using only unique data, excluding research with ready data sets Includes a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis  Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable Deep Learning techniques for their applications