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

• URL: http://arxiv.org/abs/2412.09521v1
• Date: Thu, 12 Dec 2024 18:07:23 GMT
• Title: Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Clinical Pathology Analysis
• Authors: Shengxuming Zhang, Weihan Li, Tianhong Gao, Jiacong Hu, Haoming Luo, Mingli Song, Xiuming Zhang, Zunlei Feng,
• Abstract summary: Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis. Traditional pure vision models face challenges of redundant feature extraction. Existing large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy. We propose two innovative strategies: the mixed task-guided feature enhancement, and the prompt-guided detail feature completion.
• Score: 34.199766079609795
• License:
• 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, traditional pure vision models face challenges of redundant feature extraction, whereas existing large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy. 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 490,000 samples from diverse pathology tasks-including cancer detection, grading, vascular and neural invasion identification, and so on-we trained the pathology-specialized LVLM, OmniPath. Extensive experiments demonstrate that this model significantly outperforms existing methods in diagnostic accuracy and efficiency, offering an interactive, clinically aligned approach for auxiliary diagnosis in a wide range of pathology applications.

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