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.
Transmil: Transformer based correlated multiple instance learning for whole slide image classification.Advances in Neural Information Processing Systems, 34:2136–2147, 2021
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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.