  Automatic semantic segmentation of magnetic resonance imaging (MRI) images
using deep neural networks greatly assists in evaluating and planning
treatments for various clinical applications. However, training these models is
conditioned on the availability of abundant annotated data to implement the
end-to-end supervised learning procedure. Even if we annotate enough data, MRI
images display considerable variability due to factors such as differences in
patients, MRI scanners, and imaging protocols. This variability necessitates
retraining neural networks for each specific application domain, which, in
turn, requires manual annotation by expert radiologists for all new domains. To
relax the need for persistent data annotation, we develop a method for
unsupervised federated domain adaptation using multiple annotated source
domains. Our approach enables the transfer of knowledge from several annotated
source domains to adapt a model for effective use in an unannotated target
domain. Initially, we ensure that the target domain data shares similar
representations with each source domain in a latent embedding space, modeled as
the output of a deep encoder, by minimizing the pair-wise distances of the
distributions for the target domain and the source domains. We then employ an
ensemble approach to leverage the knowledge obtained from all domains. We
provide theoretical analysis and perform experiments on the MICCAI 2016
multi-site dataset to demonstrate our method is effective.
