  Annotation scarcity and cross-modality/stain data distribution shifts are two
major obstacles hindering the application of deep learning models for nuclei
analysis, which holds a broad spectrum of potential applications in digital
pathology. Recently, unsupervised domain adaptation (UDA) methods have been
proposed to mitigate the distributional gap between different imaging
modalities for unsupervised nuclei segmentation in histopathology images.
However, existing UDA methods are built upon the assumption that data
distributions within each domain should be uniform. Based on the
over-simplified supposition, they propose to align the histopathology target
domain with the source domain integrally, neglecting severe intra-domain
discrepancy over subpartitions incurred by mixed cancer types and sampling
organs. In this paper, for the first time, we propose to explicitly consider
the heterogeneity within the histopathology domain and introduce open compound
domain adaptation (OCDA) to resolve the crux. In specific, a two-stage
disentanglement framework is proposed to acquire domain-invariant feature
representations at both image and instance levels. The holistic design
addresses the limitations of existing OCDA approaches which struggle to capture
instance-wise variations. Two regularization strategies are specifically
devised herein to leverage the rich subpartition-specific characteristics in
histopathology images and facilitate subdomain decomposition. Moreover, we
propose a dual-branch nucleus shape and structure preserving module to prevent
nucleus over-generation and deformation in the synthesized images. Experimental
results on both cross-modality and cross-stain scenarios over a broad range of
diverse datasets demonstrate the superiority of our method compared with
state-of-the-art UDA and OCDA methods.
