  Semi-supervised domain adaptation is a technique to build a classifier for a
target domain by modifying a classifier in another (source) domain using many
unlabeled samples and a small number of labeled samples from the target domain.
In this paper, we develop a semi-supervised domain adaptation method, which has
robustness to class-imbalanced situations, which are common in medical image
classification tasks. For robustness, we propose a weakly-supervised clustering
pipeline to obtain high-purity clusters and utilize the clusters in
representation learning for domain adaptation. The proposed method showed
state-of-the-art performance in the experiment using severely class-imbalanced
pathological image patches.
