Abstract
Recent developments in artificial intelligence (AI), particularly deep learning (DL) algorithms have demonstrated remarkable progress in image recognition and prediction tasks. These are now being applied in the field of radiology on medical images to quantify, characterize, classify as well as monitor various pathologies. Such DL based quantifications facilitate greater support to the visual assessment of image characteristics that is performed by the physician. Furthermore it aids in reducing interreader variability as well as assists in speeding up the radiology workflow. In this chapter, we provide an insightful motivation for employing DL based framework followed by an overview of recent applications of DL in radiology and present a systematic summary of specific DL algorithms pertaining to image perception and recognition tasks. Finally, we discuss the challenges in clinical implementation of these algorithms and provide a perspective on how the domain could be advanced in the next few years.
Keywords: Artificial intelligence, Convolutional neural nets, CT, Deep learning, MRI, Radiology, X-rays.
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