  Pathological image analysis is an important process for detecting
abnormalities such as cancer from cell images. However, since the image size is
generally very large, the cost of providing detailed annotations is high, which
makes it difficult to apply machine learning techniques. One way to improve the
performance of identifying abnormalities while keeping the annotation cost low
is to use only labels for each slide, or to use information from another
dataset that has already been labeled. However, such weak supervisory
information often does not provide sufficient performance. In this paper, we
proposed a new task setting to improve the classification performance of the
target dataset without increasing annotation costs. And to solve this problem,
we propose a pipeline that uses multiple instance learning (MIL) and domain
adaptation (DA) methods. Furthermore, in order to combine the supervisory
information of both methods effectively, we propose a method to create
pseudo-labels with high confidence. We conducted experiments on the
pathological image dataset we created for this study and showed that the
proposed method significantly improves the classification performance compared
to existing methods.
