标题： Open-PMC-18M：用于多模态表示学习的高保真大规模医学数据集 显示英文标题

标题： Open-PMC-18M: A High-Fidelity Large Scale Medical Dataset for Multimodal Representation Learning

摘要： 复合图形（由多个子图组成的多面板合成图）在生物医学文献中无处不在，但大规模子图提取仍未得到充分解决。 以往关于子图提取的工作在数据集规模和通用性方面都受到限制，留下了一个关键的开放问题：通过大规模子图提取实现的高保真图像-文本对齐如何影响视觉语言模型的表征学习？ 我们通过引入一个基于Transformer对象检测的可扩展子图提取管道解决了这一差距，该管道在由50万张复合图形组成的合成语料库上进行训练，并在ImageCLEF 2016和合成基准测试中取得了最先进的性能。 利用这个管道，我们发布了OPEN-PMC-18M，这是一个大规模高质量的生物医学视觉语言数据集，包含1800万个与临床相关的子图-标题对，涵盖放射学、显微镜和可见光摄影。 我们在精心策划的数据集上训练和评估了视觉语言模型，在检索、零样本分类和鲁棒性基准测试中表现出改进的性能，超过了现有的基线。 我们发布我们的数据集、模型和代码，以支持可重复的基准测试和进一步研究生物医学视觉语言建模和表征学习。 显示英文摘要

摘要： Compound figures, which are multi-panel composites containing diverse subfigures, are ubiquitous in biomedical literature, yet large-scale subfigure extraction remains largely unaddressed. Prior work on subfigure extraction has been limited in both dataset size and generalizability, leaving a critical open question: How does high-fidelity image-text alignment via large-scale subfigure extraction impact representation learning in vision-language models? We address this gap by introducing a scalable subfigure extraction pipeline based on transformer-based object detection, trained on a synthetic corpus of 500,000 compound figures, and achieving state-of-the-art performance on both ImageCLEF 2016 and synthetic benchmarks. Using this pipeline, we release OPEN-PMC-18M, a large-scale high quality biomedical vision-language dataset comprising 18 million clinically relevant subfigure-caption pairs spanning radiology, microscopy, and visible light photography. We train and evaluate vision-language models on our curated datasets and show improved performance across retrieval, zero-shot classification, and robustness benchmarks, outperforming existing baselines. We release our dataset, models, and code to support reproducible benchmarks and further study into biomedical vision-language modeling and representation learning.