  By promising more accurate diagnostics and individual treatment
recommendations, deep neural networks and in particular convolutional neural
networks have advanced to a powerful tool in medical imaging. Here, we first
give an introduction into methodological key concepts and resulting
methodological promises including representation and transfer learning, as well
as modelling domain-specific priors. After reviewing recent applications within
neuroimaging-based psychiatric research, such as the diagnosis of psychiatric
diseases, delineation of disease subtypes, normative modeling, and the
development of neuroimaging biomarkers, we discuss current challenges. This
includes for example the difficulty of training models on small, heterogeneous
and biased data sets, the lack of validity of clinical labels, algorithmic
bias, and the influence of confounding variables.
