Whole Slide Image (WSI) classification is often formulated as a Multiple
Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have
demonstrated remarkable performance in WSI classification. However, existing
methods leverage coarse-grained pathogenetic descriptions for visual
representation supervision, which are insufficient to capture the complex
visual appearance of pathogenetic images, hindering the generalizability of
models on diverse downstream tasks. Additionally, processing high-resolution
WSIs can be computationally expensive. In this paper, we propose a novel
"Fine-grained Visual-Semantic Interaction" (FiVE) framework for WSI
classification. It is designed to enhance the model's generalizability by
leveraging the interplay between localized visual patterns and fine-grained
pathological semantics. Specifically, with meticulously designed queries, we
start by utilizing a large language model to extract fine-grained pathological
descriptions from various non-standardized raw reports. The output descriptions
are then reconstructed into fine-grained labels used for training. By
introducing a Task-specific Fine-grained Semantics (TFS) module, we enable
prompts to capture crucial visual information in WSIs, which enhances
representation learning and augments generalization capabilities significantly.
Furthermore, given that pathological visual patterns are redundantly
distributed across tissue slices, we sample a subset of visual instances during
training. Our method demonstrates robust generalizability and strong
transferability, dominantly outperforming the counterparts on the TCGA Lung
Cancer dataset with at least 9.19% higher accuracy in few-shot experiments.

提出了一种新颖的 “细粒度的视觉 - 语义相互作用”（FiVE）框架，用于整个切片图像（WSI）的分类，通过利用局部视觉模式和细粒度病理学语义之间的相互作用，提高模型的普适性，并通过引入任务特定的细粒度语义（TFS）模块，增强表示学习和泛化能力，进一步采样训练中的视觉实例子集，表现出强大的泛化能力，在 TCGA 肺癌数据集上的少样本实验中，准确度至少比同类方法提高 9.19%。