Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study
This work addresses the issue of model robustness and generalization for medical AI applications, highlighting a critical limitation in current foundation models that could impact diagnostics and prognosis.
The study tackled the problem of batch effects in histopathological foundation models, finding that these models still contain distinct hospital signatures in feature embeddings that can cause biased predictions and misclassifications, with signatures persisting despite stain normalization and dominating feature space distances.
Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis. Yet, the lack of annotated data and the impact of batch effects, e.g., systematic technical data differences across hospitals, hamper model robustness and generalization. Recent histopathological foundation models -- pretrained on millions to billions of images -- have been reported to improve generalization performances on various downstream tasks. However, it has not been systematically assessed whether they fully eliminate batch effects. In this study, we empirically show that the feature embeddings of the foundation models still contain distinct hospital signatures that can lead to biased predictions and misclassifications. We further find that the signatures are not removed by stain normalization methods, dominate distances in feature space, and are evident across various principal components. Our work provides a novel perspective on the evaluation of medical foundation models, paving the way for more robust pretraining strategies and downstream predictors.