  This paper tries to give a gentle introduction to deep learning in medical
image processing, proceeding from theoretical foundations to applications. We
first discuss general reasons for the popularity of deep learning, including
several major breakthroughs in computer science. Next, we start reviewing the
fundamental basics of the perceptron and neural networks, along with some
fundamental theory that is often omitted. Doing so allows us to understand the
reasons for the rise of deep learning in many application domains. Obviously
medical image processing is one of these areas which has been largely affected
by this rapid progress, in particular in image detection and recognition, image
segmentation, image registration, and computer-aided diagnosis. There are also
recent trends in physical simulation, modelling, and reconstruction that have
led to astonishing results. Yet, some of these approaches neglect prior
knowledge and hence bear the risk of producing implausible results. These
apparent weaknesses highlight current limitations of deep learning. However, we
also briefly discuss promising approaches that might be able to resolve these
problems in the future.
