  What has happened in machine learning lately, and what does it mean for the
future of medical image analysis? Machine learning has witnessed a tremendous
amount of attention over the last few years. The current boom started around
2009 when so-called deep artificial neural networks began outperforming other
established models on a number of important benchmarks. Deep neural networks
are now the state-of-the-art machine learning models across a variety of areas,
from image analysis to natural language processing, and widely deployed in
academia and industry. These developments have a huge potential for medical
imaging technology, medical data analysis, medical diagnostics and healthcare
in general, slowly being realized. We provide a short overview of recent
advances and some associated challenges in machine learning applied to medical
image processing and image analysis. As this has become a very broad and fast
expanding field we will not survey the entire landscape of applications, but
put particular focus on deep learning in MRI.
  Our aim is threefold: (i) give a brief introduction to deep learning with
pointers to core references; (ii) indicate how deep learning has been applied
to the entire MRI processing chain, from acquisition to image retrieval, from
segmentation to disease prediction; (iii) provide a starting point for people
interested in experimenting and perhaps contributing to the field of machine
learning for medical imaging by pointing out good educational resources,
state-of-the-art open-source code, and interesting sources of data and problems
related medical imaging.
