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

• URL: http://arxiv.org/abs/2506.02738v2
• Date: Wed, 04 Jun 2025 12:14:31 GMT
• Title: Open-PMC-18M: A High-Fidelity Large Scale Medical Dataset for Multimodal Representation Learning
• Authors: Negin Baghbanzadeh, Sajad Ashkezari, Elham Dolatabadi, Arash Afkanpour,
• Abstract summary: We introduce a scalable subfigure extraction pipeline based on transformer-based object detection.<n>We release OPEN-PMC-18M, a large-scale high quality biomedical vision-language dataset.<n>We show improved performance across retrieval, zero-shot classification, and robustness benchmarks.
• Score: 0.03214166687856062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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