  Machine learning and computer vision methods are showing good performance in
medical imagery analysis. Yetonly a few applications are now in clinical use
and one of the reasons for that is poor transferability of themodels to data
from different sources or acquisition domains. Development of new methods and
algorithms forthe transfer of training and adaptation of the domain in
multi-modal medical imaging data is crucial for thedevelopment of accurate
models and their use in clinics. In present work, we overview methods used to
tackle thedomain shift problem in machine learning and computer vision. The
algorithms discussed in this survey includeadvanced data processing, model
architecture enhancing and featured training, as well as predicting in
domaininvariant latent space. The application of the autoencoding neural
networks and their domain-invariant variationsare heavily discussed in a
survey. We observe the latest methods applied to the magnetic resonance
imaging(MRI) data analysis and conclude on their performance as well as propose
directions for further research.
