Convolutional neural networks (CNNs) have led to significant improvements in
tasks involving semantic segmentation of images. CNNs are vulnerable in the
area of biomedical image segmentation because of distributional gap between two
source and target domains with different data modalities which leads to domain
shift. Domain shift makes data annotations in new modalities necessary because
models must be retrained from scratch. Unsupervised domain adaptation (UDA) is
proposed to adapt a model to new modalities using solely unlabeled target
domain data. Common UDA algorithms require access to data points in the source
domain which may not be feasible in medical imaging due to privacy concerns. In
this work, we develop an algorithm for UDA in a privacy-constrained setting,
where the source domain data is inaccessible. Our idea is based on encoding the
information from the source samples into a prototypical distribution that is
used as an intermediate distribution for aligning the target domain
distribution with the source domain distribution. We demonstrate the
effectiveness of our algorithm by comparing it to state-of-the-art medical
image semantic segmentation approaches on two medical image semantic
segmentation datasets.

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