The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain?
Open accessAbstract
Contrastive Language-Image Pre-training (CLIP) has shown remarkable success in learning with cross-modal supervision from extensive amounts of image-text pairs collected online.Thus far, the effectiveness of CLIP has been investigated primarily in general-domain multimodal problems.In this work, we evaluate the effectiveness of CLIP for the task of Medical Visual Question Answering (MedVQA).We present PubMedCLIP, a fine-tuned version of CLIP for the medical domain based on PubMed articles.Our experiments conducted on two MedVQA benchmark datasets illustrate that PubMed-CLIP achieves superior results improving the overall accuracy up to 3% in comparison to the state-of-the-art Model-Agnostic Meta-Learning (MAML) networks pre-trained only on visual data.The PubMedCLIP model with different back-ends, the source code for pre-training them and reproducing our MedVQA pipeline is publicly available at https://github.com/sarahESL/PubMedCLIP.
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Cites
Deep Residual Learning for Image Recognition
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
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VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
Billion-scale similarity search with GPUs
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Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
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Cited by
A visual–language foundation model for pathology image analysis using medical twitter
Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J., & Zou, J. (2023) · Nature Medicine · doi:10.1038/s41591-023-02504-3 · Cited by 693
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2306.00890 · Cited by 590
“Domain-specific pretraining has been shown to be effective for biomedical natural language processing (NLP) applications [18, 15, 11, 31] and biomedical vision-language (VL) tasks [16, 7, 41, 51, 9].”
BackgroundKonwer, A., Yang, Z., Bas, E., Xiao, C., Prasanna, P., Bhatia, P., & Kass-Hout, T. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.01955 · Cited by 17
Wang, H., & Li, Z. (2026) · arXiv · doi:10.48550/arxiv.2605.20469
“CLIP-based retrieval models such as BiomedCLIP [14] and Pub-MedCLIP [15] match images to pre-defined findings via contrastive learning, yielding deterministic outputs but limited expressiveness.”
Metadata
- Authors
- Eslami, S., Meinel, C., & de Melo, G.
- Venue
- Findings of the Association for Computational Linguistics: EACL 2023
- Published
- 2023
- Pages
- 1181-1193
- OpenAlex
- W4386566421
- Cited by
- 167
- References
- 10
- FWCI
- 18.99
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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