  Unsupervised domain adaptation (UDA) methods have been broadly utilized to
improve the models' adaptation ability in general computer vision. However,
different from the natural images, there exist huge semantic gaps for the
nuclei from different categories in histopathology images. It is still
under-explored how could we build generalized UDA models for precise
segmentation or classification of nuclei instances across different datasets.
In this work, we propose a novel deep neural network, namely Category-Aware
feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei
instance segmentation and classification. Specifically, we first propose a
category-level feature alignment module with dynamic learnable trade-off
weights. Second, we propose to facilitate the model performance on the target
data via self-supervised training with pseudo labels based on nuclei-level
prototype features. Comprehensive experiments on cross-domain nuclei instance
segmentation and classification tasks demonstrate that our approach outperforms
state-of-the-art UDA methods with a remarkable margin.
