  Since its renaissance, deep learning has been widely used in various medical
imaging tasks and has achieved remarkable success in many medical imaging
applications, thereby propelling us into the so-called artificial intelligence
(AI) era. It is known that the success of AI is mostly attributed to the
availability of big data with annotations for a single task and the advances in
high performance computing. However, medical imaging presents unique challenges
that confront deep learning approaches. In this survey paper, we first present
traits of medical imaging, highlight both clinical needs and technical
challenges in medical imaging, and describe how emerging trends in deep
learning are addressing these issues. We cover the topics of network
architecture, sparse and noisy labels, federating learning, interpretability,
uncertainty quantification, etc. Then, we present several case studies that are
commonly found in clinical practice, including digital pathology and chest,
brain, cardiovascular, and abdominal imaging. Rather than presenting an
exhaustive literature survey, we instead describe some prominent research
highlights related to these case study applications. We conclude with a
discussion and presentation of promising future directions.
