PathPT improves few-shot rare cancer subtyping by using zero-shot vision-language models to create tile-level pseudo-labels and learning prompt tokens with spatial context, outperforming standard MIL baselines when the backbone has strong zero-shot grounding.
Socioeconomic inequalities in cancer incidence and access to health services among children and adolescents in china: a cross-sectional study.The Lancet, 400(10357):1020–1032, 2022
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Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
PathPT improves few-shot rare cancer subtyping by using zero-shot vision-language models to create tile-level pseudo-labels and learning prompt tokens with spatial context, outperforming standard MIL baselines when the backbone has strong zero-shot grounding.