Boosting pathology foundation models via few-shot prompt-tuning for rare cancer subtyping

Abstract

Rare cancers comprise 20-25% of malignancies (over 70% in pediatric oncology) but face major diagnostic challenges due to limited expert availability. While pathology vision-language models show promising zero-shot capabilities for common cancers, their performance on rare cancers remains limited. Existing multi-instance learning (MIL) methods rely solely on visual features, overlooking cross-modal knowledge and compromising interpretability critical for rare cancer diagnosis. To address this, we propose PathPT, a framework that exploits vision-language foundation models through spatially-aware visual aggregation and task-specific prompt tuning. PathPT converts WSI-level supervision into fine-grained tile-level guidance, preserving tumor localization and enabling cross-modal reasoning. Across eight rare and three common cancer datasets-spanning 56 subtypes and 3958 WSIs, PathPT consistently outperforms state-of-the-art methods under data-scarce settings. It achieves substantial gains in both subtyping accuracy and cancerous region grounding ability, providing a scalable, interpretable AI solution to improve rare cancer subtyping with limited access to specialized expertise.