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.
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.JAMA, 318(22):2199–2210, 2017
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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.