Multi-Slide Views and Clustering-Inspired Memory Retrieval for Weakly Supervised Whole-Slide Image Classification

When multiple instance learning (MIL) is applied to whole-slide image (WSI) classification, the large, variable-length instance sequences prevent multiple slides from being combined within a single batch for training. The difficulty of simultaneously incorporating information from multiple independent samples is a characteristic of this field. Moreover, the pathological significance of cross-sample view associations has not been established. To address these issues, we propose MSVCR-MIL, a two-stage weakly supervised classification framework combining multi-slide views and clustering-inspired memory retrieval. In the first stage, the model learns instance representations and attention scores under slide-level supervision. Core Memory Construction (CMC) collects high-response instances from different training slides to establish morphological references from multi-slide views and constructs a compact memory through clustering-inspired K-center coreset sampling. In the second stage, Confidence-Aware Prototype Retrieval (CAPR) generates query-conditioned prototypes and retrieval confidence scores, while Backbone-Adapted Gated Fusion (BAGF) adaptively fuses the original features with the retrieved prototypes, allowing cross-slide references to contribute to slide-level prediction. During training, Dynamic Memory Refresh (DMR) updates the instance memory and periodically reconstructs the coreset, keeping the references up to date as task representations evolve. Results on three datasets covering breast cancer, lung cancer, and skin diseases demonstrate that MSVCR-MIL consistently improves WSI classification performance (mean $\Delta \mathrm{AUC}=+1.14\%$). Retrieval-distance-based prediction risk in MSVCR-MIL is consistently lower than that obtained using methods based solely on predicted probabilities (mean $\Delta \mathrm{AURC}=-4.79\%$), indicating that it can provide complementary information for identifying prediction errors. Overall, explicit cross-slide morphological references and clustering strategies offer a feasible approach to enhancing weakly supervised WSI classification and assessing prediction risk.