  Generalizability of deep learning models may be severely affected by the
difference in the distributions of the train (source domain) and the test
(target domain) sets, e.g., when the sets are produced by different hardware.
As a consequence of this domain shift, a certain model might perform well on
data from one clinic, and then fail when deployed in another. We propose a very
light and transparent approach to perform test-time domain adaptation. The idea
is to substitute the target low-frequency Fourier space components that are
deemed to reflect the style of an image. To maximize the performance, we
implement the "optimal style donor" selection technique, and use a number of
source data points for altering a single target scan appearance (Multi-Source
Transferring). We study the effect of severity of domain shift on the
performance of the method, and show that our training-free approach reaches the
state-of-the-art level of complicated deep domain adaptation models. The code
for our experiments is released.
