Multimodality Imaging, Volume 1

Deep Learning has had a major impact on the healthcare industry. With the availability of enhanced computational power and the availability of graphical processing units, deep learning in healthcare has performed better than machine learning paradigms. These improvements are further propelled by the open source nature of deep learning and lower computer hardware prices. There are great potentials in healthcare due to the increase in fully automated processes that are designed to be robust.

This reference text answers several unanswered questions in both the technical and ethical aspects of deep learning and machine learning applications in medical imaging. The book provides an introduction to deep learning and machine learning. It explores the way in which these tools can be successfully applied to different areas of diagnosis of patients through segmentation and classification medical images. The text presents some of the foundational works of application of deep learning in medical diagnosis and explores the ethicality and legal aspects of AI in medical diagnosis, such as wrong diagnosis leading to fatality. Different imaging modalities of the brain, carotid artery, heart and vascular and Covid-19 are also covered.

The text provides medical professionals with insights in identifying problems early on, offering patient treatment that is both tailored and relevant.

Key Features:

• Discusses various diseases related to lung, heart, peripheral arterial imaging, as well as gene expression characterization and classification

• Explores imaging applications, their complexities and the Deep Learning models employed to resolve them in detail

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

• Details the future of deep learning and big data in medical imaging