Polarity Prompting Vision Foundation Models for Pathology Image Analysis

作者：Yin C, Liu S, Zhou K, Wong VW, Yuen PC · 发表于：IEEE transactions on medical imaging · 年份：2025 · DOI：10.1109/TMI.2025.3578492 · 研究领域：Image Interpretation, Computer-Assisted、Humans、Algorithms、Non-alcoholic Fatty Liver Disease、Liver

The sharp rise in non-alcoholic fatty liver disease (NAFLD) cases has become a major health concern in recent years. Accurately identifying tissue alteration regions is crucial for NAFLD diagnosis but challenging with small-scale pathology datasets. Recently, prompt tuning has emerged as an effective strategy for adapting vision models to small-scale data analysis. However, current prompting techniques, designed primarily for general image classification, use generic cues that are inadequate when dealing with the intricacies of pathological tissue analysis. To solve this problem, we introduce Quantitative Attribute-based Polarity Visual Prompting (Q-PoVP), a new prompting method for pathology image analysis. Q-PoVP introduces two types of measurable attributes: K-function-based spatial attributes and histogram-based morphological attributes. Both help to measure tissue conditions quantitatively. We develop a quantitative attribute-based polarity visual prompt generator that converts quantitative visual attributes into positive and negative visual prompts, facilitating a more comprehensive and nuanced interpretation of pathological images. To enhance feature discrimination, we introduce a novel orthogonal-based polarity visual prompt tuning technique that disentangles and amplifies positive visual attributes while suppressing negative ones. We extensively tested our method on three different tasks. Our task-specific prompting demonstrates superior performance in both diagnosti...