用语言模型指导分步选图，让病理切片分析快3倍还更准。

Whole slide images (WSIs) in computational pathology pose a major computational challenge due to their gigapixel scale, often requiring tens to hundreds of thousands of high-resolution patches to be processed per slide. In conventional WSI pipelines, exhaustive high-resolution patch processing makes preprocessing far more time-consuming than downstream model training. Existing patch selection methods suffer from a fundamental paradox: all patches must still be extracted and encoded at least during training, and sometimes during both training and inference, before irrelevant ones can be discarded. To address this, we propose LanGuSTE, an efficient patch selection framework that integrates pathology-domain vision-language models (VLMs) and knowledge derived from large language models (LLMs) through two key modules: Cross- Scale Visual Prompt Tuning (CS-VPT) and coarse-to-fine patch selection. CS-VPT aligns low-resolution patches with their spatially corresponding high-resolution patches through contrastive learning, transferring fine-grained diagnostic semantics into low-resolution representations. The patch selection module then leverages VLM representations and LLM-generated pathology-specific descriptions to identify informative regions in a coarse-to-fine manner, encoding only the corresponding high-resolution patches to reduce preprocessing time. Extensive experiments demonstrate that LanGuSTE reduces overall WSI processing time to approximately 3x while achieving diagnostic performance comparable to or better than exhaustive patch processing and recent state-of-the-art patch-selection methods.