Semantic Prior-Guided Reinforced Region Reviewing for Whole Slide Image Analysis

Whole slide image (WSI) analysis is challenged by the sparsity of diagnostically relevant lesions among numerous irrelevant patches, which can dilute discriminative evidence and disperse visual attention. Vision-language models (VLMs) provide pathology semantic prior for this task. However, directly fusing such information with visual features may be unreliable because LLM-generated descriptions can be coarse, incomplete, or noisy. To address these challenges, we propose the Prior-Guided Semantic Multi-Scale Hypergraph Learning Model (PSMG), which uses LLM-generated descriptions as semantic priors for region-level WSI modeling. In the visual architecture, we first perform scale-specific hypergraph encoding to capture high-order tissue context at each magnification. Based on this, we design a semantic-guided region modeling strategy that translates textual pathology priors into region-level visual modulation. This strategy uses textual priors to highlight semantically relevant candidate regions for subsequent visual reasoning. Subsequently, we formulate a slide-level feedback mechanism to review semantically guided regions. Through task-driven region refinement, the feedback mechanism further enhances regions that contribute to prediction. Finally, we introduce a cross-magnification alignment strategy that combines unified hypergraph reasoning with knowledge distillation to reduce diagnostic inconsistency caused by scale-specific representation differences. Experiments on three public WSI benchmarks demonstrate strong classification performance, while qualitative heatmaps show more concentrated responses around annotated lesion regions. The anonymized implementation is available at https://anonymous.4open.science/r/psmg-anonymous.