Deep Learning and Its Applications in Medical Imaging

Abstract

Big data are information assets characterized by high volume, velocity, variety, and veracity. Large, multilevel, and integrated datasets offer the promise of unlocking novel insights and accelerating breakthroughs. According to an increasing interest in artificial intelligence around the world, deep learning has attracted a great deal of public attention. Deep learning techniques increase learning capacity and provide a decision support system at scales that are transforming the future of health care. Every day, deep learning algorithms are used broadly across different industries. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from intricate data. This chapter explains what deep learning is and why it is so important. We provide several examples where deep learning techniques find applications and what kind of problems can be solved using deep learning in medical imaging. This chapter also introduces a particular type of deep learning algorithm, including autoencoders, restricted Boltzmann machines, deep belief network, recurrent neural networks, and convolutional neural networks with practical variant case studies, which are at the basis of deep learning. KeywordsArtificial intelligenceDeep learning techniquesDecision support systemMedical imaging