  Annotating histopathological images is a time-consuming andlabor-intensive
process, which requires broad-certificated pathologistscarefully examining
large-scale whole-slide images from cells to tissues.Recent frontiers of
transfer learning techniques have been widely investi-gated for image
understanding tasks with limited annotations. However,when applied for the
analytics of histology images, few of them can effec-tively avoid the
performance degradation caused by the domain discrep-ancy between the source
training dataset and the target dataset, suchas different tissues, staining
appearances, and imaging devices. To thisend, we present a novel method for the
unsupervised domain adaptationin histopathological image analysis, based on a
backbone for embeddinginput images into a feature space, and a graph neural
layer for propa-gating the supervision signals of images with labels. The graph
model isset up by connecting every image with its close neighbors in the
embed-ded feature space. Then graph neural network is employed to synthesizenew
feature representation from every image. During the training stage,target
samples with confident inferences are dynamically allocated withpseudo labels.
The cross-entropy loss function is used to constrain thepredictions of source
samples with manually marked labels and targetsamples with pseudo labels.
Furthermore, the maximum mean diversityis adopted to facilitate the extraction
of domain-invariant feature repre-sentations, and contrastive learning is
exploited to enhance the categorydiscrimination of learned features. In
experiments of the unsupervised do-main adaptation for histopathological image
classification, our methodachieves state-of-the-art performance on four public
datasets
