Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study

TL;DR

This study evaluates whether recent large-scale histopathological foundation models effectively eliminate batch effects across hospitals, revealing persistent biases in feature embeddings despite their high generalization performance.

Contribution

It systematically assesses the presence of hospital-specific signatures in foundation model embeddings and highlights limitations of stain normalization in removing batch effects.

Findings

Foundation model embeddings still contain hospital signatures.

Stain normalization does not remove hospital-specific biases.

Hospital signatures influence model predictions and can cause biases.

Abstract

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.