Med-GLIP：利用大规模定位数据集推进医学语言-图像预训练
Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset
AI总结：
本文构建了含530万区域级标注、覆盖七种模态的医学定位数据集 Med-GLIP-5M，并提出模态感知框架 Med-GLIP，通过隐式层级语义学习在多个定位基准上超越现有方法，并显著提升医学 VQA 和报告生成等下游任务性能。
摘要：
医学图像定位旨在将自然语言短语与医学图像中的特定区域对齐，是智能诊断、视觉问答（VQA）和自动报告生成（MRG）的基础任务。然而，现有研究受到模态覆盖有限、标注粒度粗糙以及缺乏统一、可泛化的定位框架的制约。为应对这些挑战，我们构建了一个大规模医学定位数据集 Med-GLIP-5M，包含超过530万个区域级标注，覆盖七种成像模态，涵盖多样的解剖结构和病理发现。该数据集通过层级区域标签同时支持分割和定位任务，范围从器官级边界到细粒度病变。在此基础上，我们提出了 Med-GLIP，一个在 Med-GLIP-5M 上训练的模态感知定位框架。Med-GLIP 不依赖显式设计的专家模块，而是从多样化的训练数据中隐式获取层级语义理解，使其能够识别多粒度结构，例如区分肺与肺炎病变。大量实验表明，Med-GLIP 在多个定位基准上持续优于最先进的基线方法。此外，将其空间输出集成到下游任务（包括医学 VQA 和报告生成）中，可带来显著的性能提升。我们的数据集即将发布。
英文摘要：
Medical image grounding aims to align natural language phrases with specific regions in medical images, serving as a foundational task for intelligent diagnosis, visual question answering (VQA), and automated report generation (MRG). However, existing research is constrained by limited modality coverage, coarse-grained annotations, and the absence of a unified, generalizable grounding framework. To address these challenges, we construct a large-scale medical grounding dataset Med-GLIP-5M comprising over 5.3 million region-level annotations across seven imaging modalities, covering diverse anatomical structures and pathological findings. The dataset supports both segmentation and grounding tasks with hierarchical region labels, ranging from organ-level boundaries to fine-grained lesions. Based on this foundation, we propose Med-GLIP, a modality-aware grounding framework trained on Med-GLIP-5M. Rather than relying on explicitly designed expert modules, Med-GLIP implicitly acquires hierarchical semantic understanding from diverse training data -- enabling it to recognize multi-granularity structures, such as distinguishing lungs from pneumonia lesions. Extensive experiments demonstrate that Med-GLIP consistently outperforms state-of-the-art baselines across multiple grounding benchmarks. Furthermore, integrating its spatial outputs into downstream tasks, including medical VQA and report generation, leads to substantial performance gains. Our dataset will be released soon.
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