arXiv 2508.052442025-09-19cs.CVcs.AI

RegionMed-CLIP：一种面向医学图像理解的区域感知多模态对比学习预训练模型

RegionMed-CLIP: A Region-Aware Multimodal Contrastive Learning Pre-trained Model for Medical Image Understanding

Tianchen Fang, Guiru Liu

备注：: Upon further review, we identified that our dataset requires optimization to ensure research reliability and accuracy. Additionally, considering the target journal's latest submission policies, we believe comprehensive manuscript revisions are necessary

AI总结：

针对医学图像理解中标注数据稀缺和全局特征忽略关键病理区域的问题，本文提出区域感知多模态对比学习框架RegionMed-CLIP，通过ROI处理器融合局部与全局特征，并构建MedRegion-500k大规模区域标注语料库，在多项任务上显著超越现有视觉语言模型。

摘要：

医学图像理解在实现自动化诊断和数据驱动的临床决策支持中起着至关重要的作用。然而，其进展受到两个主要挑战的阻碍：高质量标注医学数据的有限可用性，以及对全局图像特征的过度依赖，后者常常遗漏细微但具有临床意义的病理区域。为了解决这些问题，我们提出了RegionMed-CLIP，这是一个区域感知的多模态对比学习框架，明确地将局部病理信号与整体语义表示相结合。我们方法的核心是一个创新的感兴趣区域（ROI）处理器，它自适应地将细粒度区域特征与全局上下文相融合，并辅以渐进式训练策略来增强分层多模态对齐。为了实现大规模的区域级表示学习，我们构建了MedRegion-500k，这是一个综合性的医学图像-文本语料库，具有广泛的区域标注和多层次临床描述。在图像-文本检索、零样本分类和视觉问答任务上的大量实验表明，RegionMed-CLIP始终以较大幅度超越最先进的视觉语言模型。我们的结果凸显了区域感知对比预训练的关键重要性，并将RegionMed-CLIP定位为推动多模态医学图像理解发展的稳健基础。

英文摘要：

Medical image understanding plays a crucial role in enabling automated diagnosis and data-driven clinical decision support. However, its progress is impeded by two primary challenges: the limited availability of high-quality annotated medical data and an overreliance on global image features, which often miss subtle but clinically significant pathological regions. To address these issues, we introduce RegionMed-CLIP, a region-aware multimodal contrastive learning framework that explicitly incorporates localized pathological signals along with holistic semantic representations. The core of our method is an innovative region-of-interest (ROI) processor that adaptively integrates fine-grained regional features with the global context, supported by a progressive training strategy that enhances hierarchical multimodal alignment. To enable large-scale region-level representation learning, we construct MedRegion-500k, a comprehensive medical image-text corpus that features extensive regional annotations and multilevel clinical descriptions. Extensive experiments on image-text retrieval, zero-shot classification, and visual question answering tasks demonstrate that RegionMed-CLIP consistently exceeds state-of-the-art vision language models by a wide margin. Our results highlight the critical importance of region-aware contrastive pre-training and position RegionMed-CLIP as a robust foundation for advancing multimodal medical image understanding.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。