Deep Learning in Medical Image Analysis, 1st ed. 2020 Challenges and Applications Advances in Experimental Medicine and Biology Series, Vol. 1213

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Hiroshi Fujita is a Research Professor/Emeritus Professor of Gifu University. He is a member of the Society for Medical Image Information (president), the Research Group on Medical Imaging (adviser), the Japan Society for Medical Image Engineering (director), and some other societies. His research interests include computer-aided diagnosis system, image analysis and processing, and image evaluation in medicine. He has published over 1000 papers in Journals, Proceedings,Book chapters and Scientific Magazines.

Highlights issues and challenges of deep learning, specifically in medical imaging problems, surveying and discussing practical approaches in general and in the context of specific problems Describes cutting-edge research and application of deep learning in a broad range of medical imaging scenarios such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems Provides insights in employing deep learning models for different medical tasks and scenarios as well as exploiting these novel approaches in emerging areas of research