PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents

• URL: http://arxiv.org/abs/2303.07240v1
• Date: Mon, 13 Mar 2023 16:13:16 GMT
• Title: PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents
• Authors: Weixiong Lin, Ziheng Zhao, Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Yanfeng Wang, Weidi Xie
• Abstract summary: We build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset. PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level. While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP achieves state-of-the-art results on various downstream tasks.
• Score: 35.64805788623848
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset, which is 8 times larger than before. PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption. While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP achieves state-of-the-art results on various downstream tasks, including image-text retrieval on ROCO, MedMNIST image classification, Medical VQA, i.e. +8.1% R@10 on image-text retrieval, +3.9% accuracy on image classification.

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