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CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts

Kieu-Anh Truong Thi, Huy-Hieu Pham, Duc-Trong Le

TL;DR

This work tackles the out-of-distribution problem in histopathology by reframing tumor detection as a causal inference task and employing front-door adjustment to mitigate confounding from domain-specific factors. It introduces CPIT, which blends Fourier-based texture shifts and stain normalization to simulate plausible interventions while preserving semantic content, enabling estimation of the interventional distribution $P(Y|do(X))$ via $P(S|x)$ and $P(Y|do(S))$. The approach is formalized with explicit causal equations and an optimization loss that fuses transformed predictions with a residual from the original input. Empirical results on CAMELYON17 and PHIST show consistent, sometimes substantial, improvements over baselines under domain shifts, highlighting the practical potential of causal reasoning for robust histopathology analysis.

Abstract

Domain shift in histopathology, often caused by differences in acquisition processes or data sources, poses a major challenge to the generalization ability of deep learning models. Existing methods primarily rely on modeling statistical correlations by aligning feature distributions or introducing statistical variation, yet they often overlook causal relationships. In this work, we propose a novel causal-inference-based framework that leverages semantic features while mitigating the impact of confounders. Our method implements the front-door principle by designing transformation strategies that explicitly incorporate mediators and observed tissue slides. We validate our method on the CAMELYON17 dataset and a private histopathology dataset, demonstrating consistent performance gains across unseen domains. As a result, our approach achieved up to a 7% improvement in both the CAMELYON17 dataset and the private histopathology dataset, outperforming existing baselines. These results highlight the potential of causal inference as a powerful tool for addressing domain shift in histopathology image analysis.

CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts

TL;DR

This work tackles the out-of-distribution problem in histopathology by reframing tumor detection as a causal inference task and employing front-door adjustment to mitigate confounding from domain-specific factors. It introduces CPIT, which blends Fourier-based texture shifts and stain normalization to simulate plausible interventions while preserving semantic content, enabling estimation of the interventional distribution via and . The approach is formalized with explicit causal equations and an optimization loss that fuses transformed predictions with a residual from the original input. Empirical results on CAMELYON17 and PHIST show consistent, sometimes substantial, improvements over baselines under domain shifts, highlighting the practical potential of causal reasoning for robust histopathology analysis.

Abstract

Domain shift in histopathology, often caused by differences in acquisition processes or data sources, poses a major challenge to the generalization ability of deep learning models. Existing methods primarily rely on modeling statistical correlations by aligning feature distributions or introducing statistical variation, yet they often overlook causal relationships. In this work, we propose a novel causal-inference-based framework that leverages semantic features while mitigating the impact of confounders. Our method implements the front-door principle by designing transformation strategies that explicitly incorporate mediators and observed tissue slides. We validate our method on the CAMELYON17 dataset and a private histopathology dataset, demonstrating consistent performance gains across unseen domains. As a result, our approach achieved up to a 7% improvement in both the CAMELYON17 dataset and the private histopathology dataset, outperforming existing baselines. These results highlight the potential of causal inference as a powerful tool for addressing domain shift in histopathology image analysis.
Paper Structure (14 sections, 12 equations, 2 figures, 2 tables)

This paper contains 14 sections, 12 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Causal learning framework
  4. Causal-Preserving Interventional Transformation
  5. Fourier Transform
  6. Stain Normalization
  7. Optimization
  8. Experiments
  9. Datasets
  10. Experimental Results
  11. Quantitative and Qualitative Results
  12. Ablation Studies
  13. Conclusion
  14. Acknowledgements

Figures (2)

  • Figure 1: a, c, Example images from training and test datasets. Stain variation can observed between domains. b, Causal graph represent the causal relationship among the confounder C, the input image X, the mediator S and the label Y
  • Figure 2: Overall architecture of CLEAR (left) and qualitative results across three scanners on the CAMELYON17 dataset (right). Correctly predicted examples are shown in comparison with the baseline.