Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis

• URL: http://arxiv.org/abs/2412.09521v3
• Date: Fri, 16 May 2025 10:17:33 GMT
• Title: Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis
• Authors: Shengxuming Zhang, Weihan Li, Tianhong Gao, Jiacong Hu, Haoming Luo, Xiuming Zhang, Jing Zhang, Mingli Song, Zunlei Feng,
• Abstract summary: Large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy in pathology image analysis.<n>We propose two innovative strategies: the mixed task-guided feature enhancement, and the prompt-guided detail feature completion.<n>We trained the pathology-specialized LVLM, OmniPath, which significantly outperforms existing methods in diagnostic accuracy and efficiency.
• Score: 37.11302829771659
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis, relying heavily on detailed, multi-scale analysis of high-resolution whole slide images (WSI). However, existing large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy in pathology image analysis. To overcome these issues, we propose two innovative strategies: the mixed task-guided feature enhancement, which directs feature extraction toward lesion-related details across scales, and the prompt-guided detail feature completion, which integrates coarse- and fine-grained features from WSI based on specific prompts without compromising inference speed. Leveraging a comprehensive dataset of 490K samples from diverse pathology tasks, we trained the pathology-specialized LVLM, OmniPath. Extensive experiments demonstrate that this model significantly outperforms existing methods in diagnostic accuracy and efficiency, providing an interactive, clinically aligned approach for auxiliary diagnosis in a wide range of pathology applications.

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