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.

本文提出了一种基于 Cycle Consistency Panoptic Domain Adaptive Mask R-CNN 架构的无监督核分割方法，该方法利用荧光显微镜图像进行学习，并引入对源域的偏置特征的动态修正机制，以实现跨数据集的域适应，实验结果表明，该方法在三个数据集上优于最先进的无监督域自适应方法，同时表现出类似于完全监督方法的性能。