  Unsupervised domain adaptation (UDA) for nuclei instance segmentation is
important for digital pathology, as it alleviates the burden of labor-intensive
annotation and domain shift across datasets. In this work, we propose a Cycle
Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for
unsupervised nuclei segmentation in histopathology images, by learning from
fluorescence microscopy images. More specifically, we first propose a nuclei
inpainting mechanism to remove the auxiliary generated objects in the
synthesized images. Secondly, a semantic branch with a domain discriminator is
designed to achieve panoptic-level domain adaptation. Thirdly, in order to
avoid the influence of the source-biased features, we propose a task
re-weighting mechanism to dynamically add trade-off weights for the
task-specific loss functions. Experimental results on three datasets indicate
that our proposed method outperforms state-of-the-art UDA methods
significantly, and demonstrates a similar performance as fully supervised
methods.
