Explainable artificial intelligence (XAI) is increasingly important for computational pathology, where reliable and interpretable predictions are required for clinical use. Pre-trained Vision-Language Models (VLMs) offer a natural pathway to connect visual evidence with textual concepts, but adapting them to the domain of cancer pathology remains challenging due to fine-grained and heterogeneous morphological patterns. Visual prompt learning enables efficient task adaptation with minimal fine-tuning; however, existing prompting techniques face critical limitations: soft prompts often lack clinical specificity, while manually designed hard prompts reduce adaptability. To address these issues, we propose RAPT, a retrieval-augmented and text-guided visual prompting framework for explainable pathology classification. Given an input image, RAPT retrieves semantically related exemplars and leverages class-specific textual descriptions to construct disease-aware prompt tokens, injecting diagnostic cues without manual annotation. An adaptive weighting mechanism attenuates unreliable retrieval evidence, and bridge prompt tokens facilitate the integration of retrieved cues with image representations. Extensive experiments on three public cancer pathology datasets (PatchGastric, BACH, and LC25000) demonstrate that RAPT consistently outperforms prompting baselines across diverse backbone settings. Additional analyses and qualitative case studies demonstrate clinically actionable cues, robustness to imperfect retrieval, and clear failure-mode boundaries for trustworthy pathology decision support, highlighting the potential of robust and explainable prompting for computational pathology.
Hou et al. (Thu,) studied this question.