Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View
<|reference_start|>Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View: 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.<|reference_end|>
@article{fan2024revisiting,
    title={Revisiting Adaptive Cellular Recognition Under Domain Shifts: A
  Contextual Correspondence View},
    author={Jianan Fan, Dongnan Liu, Canran Li, Hang Chang, Heng Huang, Filip
  Braet, Mei Chen, and Weidong Cai},
    journal={arXiv preprint arXiv:2407.12870},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12870},
    primaryClass={q-bio.QM cs.LG eess.IV}
}