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<p>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 <b>PathPT</b>, 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月11日

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  • Dexuan He,
  • Xiao Zhou,
  • Wenbin Guan,
  • Liyuan Zhang,
  • Xiaoman Zhang,
  • Sinuo Xu,
  • Ge Wang,
  • Lifeng Wang,
  • Xiaojun Yuan,
  • Jing Ma,
  • Xin Sun,
  • Yanfeng Wang,
  • Kun Sun,
  • Ya Zhang,
  • Weidi Xie

摘要

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.