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    <abstract>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 PubMedCLIP 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.</abstract>
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%0 Conference Proceedings
%T PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain?
%A Eslami, Sedigheh
%A Meinel, Christoph
%A de Melo, Gerard
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F eslami-etal-2023-pubmedclip
%X 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 PubMedCLIP 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.
%R 10.18653/v1/2023.findings-eacl.88
%U https://aclanthology.org/2023.findings-eacl.88
%U https://doi.org/10.18653/v1/2023.findings-eacl.88
%P 1181-1193

Markdown (Informal)

[PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain?](https://aclanthology.org/2023.findings-eacl.88) (Eslami et al., Findings 2023)

• PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain? (Eslami et al., Findings 2023)

ACL

• Sedigheh Eslami, Christoph Meinel, and Gerard de Melo. 2023. PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain?. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1181–1193, Dubrovnik, Croatia. Association for Computational Linguistics.