MUSE: Harnessing Precise and Diverse Semantics for Few-Shot Whole Slide Image Classification

TL;DR

Experiments show that MUSE consistently outperforms existing vision-language baselines in few-shot settings, demonstrating that effective few-shot pathology learning requires not only richer semantic sources but also their active and sample-aware semantic optimization.

Abstract

In computational pathology, few-shot whole slide image classification is primarily driven by the extreme scarcity of expert-labeled slides. Recent vision-language methods incorporate textual semantics generated by large language models, but treat these descriptions as static class-level priors that are shared across all samples and lack sample-wise refinement. This limits both the diversity and precision of visual-semantic alignment, hindering generalization under limited supervision. To overcome this, we propose the stochastic MUlti-view Semantic Enhancement (MUSE), a framework that first refines semantic precision via sample-wise adaptation and then enhances semantic richness through retrieval-augmented multi-view generation. Specifically, MUSE introduces Sample-wise Fine-grained Semantic Enhancement (SFSE), which yields a fine-grained semantic prior for each sample through MoE-based adaptive visual-semantic interaction. Guided by this prior, Stochastic Multi-view Model Optimization (SMMO) constructs an LLM-generated knowledge base of diverse pathological descriptions per class, then retrieves and stochastically integrates multiple matched textual views during training. These dynamically selected texts serve as enriched semantic supervisions to stochastically optimize the vision-language model, promoting robustness and mitigating overfitting. Experiments on three benchmark WSI datasets show that MUSE consistently outperforms existing vision-language baselines in few-shot settings, demonstrating that effective few-shot pathology learning requires not only richer semantic sources but also their active and sample-aware semantic optimization. Our code is available at: https://github.com/JiahaoXu-god/CVPR2026_MUSE.

Figures (15)

• Figure 1: Comparison of MUSE with existing VLM-based MIL methods. (a) VLM-based MIL methods incorporate pathological text and enable cross-modal interaction between text and image modalities. (b) Our method performs fine-grained modeling of semantics and enables interaction between text and image modalities, while enhancing text diversity through knowledge base retrieval and stochastic optimization.
• Figure 2: Overview of the proposed MUSE framework. (DSR: Decompositional Semantic Refinement. SVTI: Sample-wise Vision-Text Interaction) (a) The input semantic information is decomposed and modeled in a fine-grained manner. We then leverage the refined semantic representations to extract sample-relevant visual-semantic information and facilitate cross-modal interaction. (b) The semantic-enhanced features are used to retrieve relevant texts from the multi-view text knowledge base, and these retrieved texts are subsequently leveraged through stochastic optimization to enrich semantic diversity.
• Figure 3: Our pipeline of the category related text knowledge base generation. (a) We employ ChatGPT to analyze and decompose the concepts associated with class names. (b) We leverage ChatGPT to construct concrete examples for each aspect. (c) We randomly sample some examples and combine them with prompts to guide the locally deployed LLM in generating the category knowledge base.
• Figure 4: The visualization analysis of fine-grained semantic priors via SFSE and sample-based retrieval. (a) Visualization of fine-grained semantic in sample embedding space. (b) Comparison of text features retrieved by different samples. (Different colors represent different samples.)
• Figure 5: Visualization of the regions attended by individual experts.