  Domain Adaptation (DA) methods are widely used in medical image segmentation
tasks to tackle the problem of differently distributed train (source) and test
(target) data. We consider the supervised DA task with a limited number of
annotated samples from the target domain. It corresponds to one of the most
relevant clinical setups: building a sufficiently accurate model on the minimum
possible amount of annotated data. Existing methods mostly fine-tune specific
layers of the pretrained Convolutional Neural Network (CNN). However, there is
no consensus on which layers are better to fine-tune, e.g. the first layers for
images with low-level domain shift or the deeper layers for images with
high-level domain shift. To this end, we propose SpotTUnet - a CNN architecture
that automatically chooses the layers which should be optimally fine-tuned.
More specifically, on the target domain, our method additionally learns the
policy that indicates whether a specific layer should be fine-tuned or reused
from the pretrained network. We show that our method performs at the same level
as the best of the nonflexible fine-tuning methods even under the extreme
scarcity of annotated data. Secondly, we show that SpotTUnet policy provides a
layer-wise visualization of the domain shift impact on the network, which could
be further used to develop robust domain generalization methods. In order to
extensively evaluate SpotTUnet performance, we use a publicly available dataset
of brain MR images (CC359), characterized by explicit domain shift. We release
a reproducible experimental pipeline.
