The multi-classification of histopathological images under imbalanced sample conditions remains a long-standing unresolved challenge in computational pathology. In this paper, we propose for the first time a cross-patient pseudo-bag generation technique to address this challenge. Our key innovation lies in a cross-patient pseudo-bag generation framework that extracts complementary pathological features to construct distributionally consistent pseudo-bags. To resolve the critical challenge of distributional alignment in pseudo-bag generation, we propose an affinity-driven curriculum contrastive learning strategy, integrating sample affinity metrics with progressive training to stabilize representation learning. Unlike prior methods focused on bag-level embeddings, our framework pioneers a paradigm shift toward multi-instance feature distribution mining, explicitly modeling inter-bag heterogeneity to address class imbalance. Our method demonstrates significant performance improvements on three datasets with multiple classification difficulties, outperforming the second-best method by an average of 1.95 percentage points in F1 score and 2.07 percentage points in ACC.
Wu et al. (Wed,) studied this question.