Center-Primed Multiple Instance Learning for Reducing Shortcuts in Cross-Center Whole-Slide Image Classification

Weakly supervised multiple instance learning (MIL) requires only slide-level labels with low annotation labor costs and is widely used for whole-slide image (WSI) classification. However, inter-hospital differences in staining, scanning, and patient composition may cause models to rely on center-related cues that are spuriously correlated with disease labels, leading to reduced cross-center classification performance. To meet this challenge, this study proposes CenPrime-MIL, which uses fixed center Primes and zero–real Prime dual-path learning with a shared task head to establish an auxiliary pathway for center-related information that can be removed at inference. A Prime-difference center identification constraint directs center-related changes into this auxiliary pathway, while class-conditional center adversarial regularization suppresses center-predictive information in bag-level representations while also preserving pathological discriminative information. Evaluation on three multicenter datasets of breast cancer and skin diseases shows that CenPrime-MIL consistently improves the classification performance of the base MIL models, with average gains of 3.86% in AUC and 3.79% in ACC. Conditional representation analysis further shows that CenPrime-MIL more effectively disentangles center-related encodings ($\Delta \mathrm{AUC}=-10.50\%$) and diagnostic encodings ($\Delta \mathrm{AUC}=+4.88\%$), supporting its ability to enhance pathological discriminative while reducing the influence of center information. Overall, CenPrime-MIL provides an effective approach to improving the reliability of cross-center WSI classification.