Multimodality Imaging: Deep learning applications. Volume 1

This research and reference text explores the finer details of deep learning models. It provides a brief outline on popular models including convolution neural networks, deep belief networks, autoencoders and residual neural networks. This research and reference text explores the finer details of Deep Learning models. It provides a brief outline on popular models including convolution neural networks (CNN), deep belief networks (DBN), autoencoders, residual neural networks (Res Nets). The text discusses some of the Deep Learning-based applications in gene identification. Sections in the book explore the foundation and necessity of deep learning in radiology, the application of deep learning in the area of cardiovascular imaging and deep learning applications in the area of fatty liver disease characterization and COVID19, respectively. This reference text is highly relevant for medical professionals and researchers in the area of AI in medical imaging. Key Features:Discusses various diseases related to lung, heart, peripheral arterial imaging, as well as gene expression characterization and classificationExplores imaging applications, their complexities and the Deep Learning models employed to resolve them in detailProvides state-of-the-art contributions while addressing doubts in multimodal researchDetails the future of deep learning and big data in medical imaging