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Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

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.

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Author information

Authors and Affiliations

  1. University of Sydney, Camperdown, NSW, 2050, Australia

    Jianan Fan, Dongnan Liu, Canran Li, Filip Braet & Weidong Cai

  2. Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

    Hang Chang

  3. University of Maryland at College Park, College Park, MD, 20742, USA

    Heng Huang

  4. Microsoft, Redmond, WA, 98052, USA

    Mei Chen

Authors
  1. Jianan Fan
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Corresponding author

Correspondence to Jianan Fan .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Fan, J. et al. (2025). Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15131. Springer, Cham. https://doi.org/10.1007/978-3-031-73464-9_17

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  • DOI: https://doi.org/10.1007/978-3-031-73464-9_17

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