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

• URL: http://arxiv.org/abs/2503.10731v1
• Date: Thu, 13 Mar 2025 12:18:37 GMT
• Title: Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images
• Authors: Md Mamunur Rahaman, Ewan K. A. Millar, Erik Meijering,
• Abstract summary: We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification.<n>We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings.<n>A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
• Score: 7.048241543461529
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot learning holds tremendous potential for histopathology image analysis by enabling models to generalize to unseen classes without extensive labeled data. Recent advancements in vision-language models (VLMs) have expanded the capabilities of ZSL, allowing models to perform tasks without task-specific fine-tuning. However, applying VLMs to histopathology presents considerable challenges due to the complexity of histopathological imagery and the nuanced nature of diagnostic tasks. In this paper, we propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification. MR-PHE leverages multiresolution patch extraction to mimic the diagnostic workflow of pathologists, capturing both fine-grained cellular details and broader tissue structures critical for accurate diagnosis. We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings, effectively combining local and global contextual information. Additionally, we develop a comprehensive prompt generation and selection framework, enriching class descriptions with domain-specific synonyms and clinically relevant features to enhance semantic understanding. A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings, emphasizing diagnostically important regions during classification. Our approach utilizes pretrained VLM, CONCH for ZSL without requiring domain-specific fine-tuning, offering scalability and reducing dependence on large annotated datasets. Experimental results demonstrate that MR-PHE not only significantly improves zero-shot classification performance on histopathology datasets but also often surpasses fully supervised models.

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