  Cellular nuclei recognition serves as a fundamental and essential step in the
workflow of digital pathology. However, with disparate source organs and
staining procedures among histology image clusters, the scanned tiles
inherently conform to a non-uniform data distribution, which induces
deteriorated promises for general cross-cohort usages. Despite the latest
efforts leveraging domain adaptation to mitigate distributional discrepancy,
those methods are subjected to modeling the morphological characteristics of
each cell individually, disregarding the hierarchical latent structure and
intrinsic contextual correspondences across the tumor micro-environment. In
this work, we identify the importance of implicit correspondences across
biological contexts for exploiting domain-invariant pathological composition
and thereby propose to exploit the dependence over various biological
structures for domain adaptive cellular recognition. We discover those
high-level correspondences via unsupervised contextual modeling and use them as
bridges to facilitate adaptation over diverse organs and stains. In addition,
to further exploit the rich spatial contexts embedded amongst nuclear
communities, we propose self-adaptive dynamic distillation to secure
instance-aware trade-offs across different model constituents. The proposed
method is extensively evaluated on a broad spectrum of cross-domain settings
under miscellaneous data distribution shifts and outperforms the
state-of-the-art methods by a substantial margin. Code is available at
https://github.com/camwew/CellularRecognition_DA_CC.
