One Slide, Many Views: Unifying Complementary Foundation Model Perspectives for WSI Analysis

Recent advances in foundation models (FMs) have led to aproliferation of Whole Slide Image (WSI) feature extractors, yet WSIanalysis still faces a practical challenge: no single FM consistently per-forms best across all tasks, and the performance gap among differentMultiple Instance Learning (MIL) aggregators built on a single strongFM can become limited in some settings. Recent research further showsthat simple ensembles of FMs can outperform the best individual FM,indicating that different FMs capture partially overlapping yet comple-mentary information. This motivates a shift from selecting a single FMtoward coordinating and exploiting multiple FMs jointly. However, exist-ing multi-FM approaches mostly rely on naive feature concatenation orpatch-level selection, operating on raw correlated FM features withoutexplicitly disentangling shared and view-specific information. To addressthis challenge, we reformulate FM ensembling as a Multi-View Learn-ing (MVL) problem. We propose Multi-View Multiple Instance Learning(MV-MIL), a plug-and-play framework that treats features from eachFM as a distinct view of a WSI. MV-MIL introduces an information-theoretic disentanglement module to exploit cross-view complementar-ity while suppressing redundancy, together with a Multi-Branch Predic-tion Head that integrates shared and view-specific representations withinMIL pipelines. Extensive experiments on 14 public benchmarks showthat MV-MIL consistently achieves state-of-the-art performance, withits plug-and-play design yielding robust gains across diverse MIL meth-ods and FM combinations.