Sources of Batch Effects in Histopathology
Histopathology image analysis frequently encounters batch effects, which are systematic variations arising from differences in experimental conditions rather than genuine biological changes. These variations can originate from both technical and biological sources [1, 2]. Technical batch effects typically stem from inconsistencies during sample preparation (e. g., fixation and staining protocols), imaging processes (scanner types, resolution, and postprocessing), and artifacts such as tissue folds or coverslip misplacements. Biological batch effects, on the other hand, result from disease or patient-specific covariates like disease progression stage, age, sex, or race.
Batch effects pose significant problems in histopathological image analysis, as they can mask actual biological differences between samples, introduce false correlations, and impair model accuracy and generalization [3–7]. Therefore, batch correction methods aim at addressing technical variations while keeping biological signals intact. However, distinguishing between technical and biological sources remains challenging. Similarly, eliminating technical batch effects completely is rarely feasible, especially in multi-site studies involving heterogeneous conditions and populations [8, 9]. Extensive studies and methods addressing batch effect correction have been developed in domains such as single-cell RNA sequencing (e. g., ComBat [10], BBKNN [11], Harmony [12], Scanorama [13]), but these techniques are tailored for tabular data, limiting their direct application to histopathology.
The Age of Foundation Models for Pathology: Are They Robust to Clinical Domains?
Foundation models in pathology have demonstrated large performance gains on downstream tasks through self-supervised learning on large-scale datasets
[14, 15]. However, batch effects are not analyzed systematically despite their frequent occurrence [16]. Recently, studies have shown that models are potentially not robust to clinical site-specific effects [8, 9, 17], especially on difficult tasks like mutation prediction or cancer-staging from pathology images. Here, we advocate for including a systematic batch effect analysis in histopathology workflows by visualizing and quantifying batch effects associated with known covariates.
In particular, low-dimensional feature representations should be analyzed in connection with metadata, including technical variations (covariates) for each image, such as the clinical site, experiment number, staining protocols, or scanners and biological labels (Fig. 2a–d).
