CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts
This work addresses domain shift challenges in histopathology image analysis, which is crucial for improving generalization in medical AI applications, though it appears incremental by applying causal inference to a known bottleneck.
The paper tackled the problem of domain shift in histopathology tumor detection by proposing a causal-inference-based framework that leverages semantic features and mitigates confounders, achieving up to a 7% improvement in performance on datasets like CAMELYON17 and a private dataset compared to existing baselines.
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.