Multimodality Imaging, Volume 1 (Original PDF from Publisher)

This research and referencetext explores the finer details of Deep Learning models. It provides a briefoutline on popular models including convolution neural networks (CNN), deepbelief networks (DBN), autoencoders, residual neural networks (Res Nets). Thetext discusses someof the Deep Learning-based applications in gene identification. Sections in thebook explore the foundation and necessity of deep learning in radiology, theapplication of deep learning in the area of cardiovascular imaging and deep learningapplications in the area of fatty liver disease characterization and COVID19,respectively.

This reference text is highly relevantfor medical professionals and researchers in the area of AI in medical imaging.

Key Features:

• Discussesvarious diseases related to lung, heart, peripheral arterial imaging, as wellas gene expression characterization and classification

• Exploresimaging applications, their complexities and the Deep Learning models employedto resolve them in detail

• Providesstate-of-the-art contributions while addressing doubts in multimodal research

• Detailsthe future of deep learning and big data in medical imaging