Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images

Area of Science:

• Computational pathology
• Digital pathology
• Artificial intelligence in medicine

Background:

• Zero-shot learning (ZSL) enables generalization to new classes without labeled data, crucial for histopathology.
• Vision-language models (VLMs) show promise for ZSL but face challenges with complex histopathology images and nuanced diagnostics.

Purpose of the Study:

• To develop a novel framework, Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE), for effective zero-shot histopathology image classification.
• To improve the semantic understanding and classification performance of models on unseen histopathological classes.

Main Methods:

• MR-PHE employs multiresolution patch extraction to capture cellular and tissue features, mimicking pathologist workflows.
• A hybrid embedding strategy combines global image and weighted patch embeddings for integrated local and global context.
• A prompt generation framework enriches class descriptions with domain-specific terms, and a similarity-based patch weighting mechanism highlights diagnostically relevant regions.

Main Results:

• MR-PHE significantly enhances zero-shot classification performance on histopathology datasets.
• The framework demonstrates effectiveness in generalizing to unseen classes, a key challenge in computational pathology.
• Performance often surpasses that of fully supervised models, indicating strong potential for clinical application.

Conclusions:

• MR-PHE offers a powerful approach for zero-shot histopathology classification, addressing limitations of current VLM applications.
• The method's ability to integrate multiresolution features and enhanced semantic understanding drives its superior performance.
• This framework holds significant potential for advancing computational pathology and diagnostic accuracy.