The paper "Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction" by Hao Li et al. addresses the challenges in whole slide image (WSI) classification, particularly the limitations of coarse-grained pathogenetic descriptions in Vision-Language Models (VLMs). The authors propose a novel framework called FiVE (Fine-grained Visual-Semantic Interaction) to enhance the generalizability and computational efficiency of WSI classification models. FiVE leverages fine-grained pathological descriptions extracted from non-standardized pathology reports using a large language model (LLM) like GPT-4. These descriptions are then reconstructed into fine-grained labels for training. The framework includes a Task-specific Fine-grained Semantics (TFS) module that captures crucial visual information in WSIs by introducing fine-grained guidance during training. Additionally, a patch sampling strategy is employed to reduce computational costs while maintaining accuracy. The method demonstrates robust generalizability and strong transferability, outperforming existing methods on the TCGA Lung Cancer dataset with at least 9.19% higher accuracy in few-shot experiments. The code for FiVE is available at: https://github.com/fs1rius/WSI_FiVE.The paper "Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction" by Hao Li et al. addresses the challenges in whole slide image (WSI) classification, particularly the limitations of coarse-grained pathogenetic descriptions in Vision-Language Models (VLMs). The authors propose a novel framework called FiVE (Fine-grained Visual-Semantic Interaction) to enhance the generalizability and computational efficiency of WSI classification models. FiVE leverages fine-grained pathological descriptions extracted from non-standardized pathology reports using a large language model (LLM) like GPT-4. These descriptions are then reconstructed into fine-grained labels for training. The framework includes a Task-specific Fine-grained Semantics (TFS) module that captures crucial visual information in WSIs by introducing fine-grained guidance during training. Additionally, a patch sampling strategy is employed to reduce computational costs while maintaining accuracy. The method demonstrates robust generalizability and strong transferability, outperforming existing methods on the TCGA Lung Cancer dataset with at least 9.19% higher accuracy in few-shot experiments. The code for FiVE is available at: https://github.com/fs1rius/WSI_FiVE.