  Current multi-instance learning algorithms for pathology image analysis often
require a substantial number of Whole Slide Images for effective training but
exhibit suboptimal performance in scenarios with limited learning data. In
clinical settings, restricted access to pathology slides is inevitable due to
patient privacy concerns and the prevalence of rare or emerging diseases. The
emergence of the Few-shot Weakly Supervised WSI Classification accommodates the
significant challenge of the limited slide data and sparse slide-level labels
for diagnosis. Prompt learning based on the pre-trained models (\eg, CLIP)
appears to be a promising scheme for this setting; however, current research in
this area is limited, and existing algorithms often focus solely on patch-level
prompts or confine themselves to language prompts. This paper proposes a
multi-instance prompt learning framework enhanced with pathology knowledge,
\ie, integrating visual and textual prior knowledge into prompts at both patch
and slide levels. The training process employs a combination of static and
learnable prompts, effectively guiding the activation of pre-trained models and
further facilitating the diagnosis of key pathology patterns. Lightweight
Messenger (self-attention) and Summary (attention-pooling) layers are
introduced to model relationships between patches and slides within the same
patient data. Additionally, alignment-wise contrastive losses ensure the
feature-level alignment between visual and textual learnable prompts for both
patches and slides. Our method demonstrates superior performance in three
challenging clinical tasks, significantly outperforming comparative few-shot
methods.
