OGPCMIL: Omics-Guided Prototype Calibration Multimodal Multiple Instance Learning for Few-Shot Whole Slide Image Subtyping of Rare Cancers

Accurate subtype classification of rare cancers is essential for individualized clinical treatment. However, rare cancers suffer from limited cases and scarce annotations, so unimodal pathological data fail to capture subtype-specific features and stably distinguish fine-grained subtypes. Multimodal learning integrating whole slide images (WSIs) and genomic data provides a feasible solution, yet existing multimodal and multi-instance learning methods still have key limitations for few-shot rare cancer subtyping: (1) Most methods directly establish dense cross-modal interactions between high-dimensional omics features and numerous image patches through global fusion. Such operations easily cause overfitting under limited labeled data and fail to capture stable subtype distinctions. (2) Genomic profiles reflect the overall molecular state of patients, while subtype-specific pathological features only exist in local WSI regions. Current approaches cannot effectively connect global omics priors with local patch features, resulting in underutilization of cross-modal complementary information. To address these issues, we propose OGPCMIL, an Omics-Guided Prototype Calibration Multimodal Multiple Instance Learning framework for rare cancer subtyping. We first adopt a low-rank prototype calibration mechanism to transform high-dimensional omics information into subtle and reliable subtype prototype adjustments, effectively avoiding overfitting in few-shot cross-modal fusion. Furthermore, we design a class-conditional attention aggregation mechanism, which delivers global omics priors to local patches guided by calibrated prototypes, establishing precise associations between omics signatures and subtype-related pathological regions. Extensive 1-shot, 5-shot and 10-shot experiments on three rare cancer datasets show that OGPCMIL outperforms all compared baselines in 18 of 27 settings. Ablation studies validate the effectiveness of our core modules.