  The domain shift in pathological segmentation is an important problem, where
a network trained by a source domain (collected at a specific hospital) does
not work well in the target domain (from different hospitals) due to the
different image features. Due to the problems of class imbalance and different
class prior of pathology, typical unsupervised domain adaptation methods do not
work well by aligning the distribution of source domain and target domain. In
this paper, we propose a cluster entropy for selecting an effective whole slide
image (WSI) that is used for semi-supervised domain adaptation. This approach
can measure how the image features of the WSI cover the entire distribution of
the target domain by calculating the entropy of each cluster and can
significantly improve the performance of domain adaptation. Our approach
achieved competitive results against the prior arts on datasets collected from
two hospitals.
