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    <abstract>Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction. However, medical image-text datasets are orders of magnitude below the general images and captions from the internet. Moreover, previous methods encounter many false negatives, i.e., images and reports from separate patients probably carry the same semantics but are wrongly treated as negatives. In this paper, we decouple images and texts for multimodal contrastive learning, thus scaling the usable training data in a combinatorial magnitude with low cost. We also propose to replace the InfoNCE loss with semantic matching loss based on medical knowledge to eliminate false negatives in contrastive learning. We prove that MedCLIP is a simple yet effective framework: it outperforms state-of-the-art methods on zero-shot prediction, supervised classification, and image-text retrieval. Surprisingly, we observe that with only 20K pre-training data, MedCLIP wins over the state-of-the-art method (using 200K data). The code is available at https://github.com/RyanWangZf/MedCLIP.</abstract>
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%0 Conference Proceedings
%T MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
%A Wang, Zifeng
%A Wu, Zhenbang
%A Agarwal, Dinesh
%A Sun, Jimeng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wang-etal-2022-medclip
%X Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction. However, medical image-text datasets are orders of magnitude below the general images and captions from the internet. Moreover, previous methods encounter many false negatives, i.e., images and reports from separate patients probably carry the same semantics but are wrongly treated as negatives. In this paper, we decouple images and texts for multimodal contrastive learning, thus scaling the usable training data in a combinatorial magnitude with low cost. We also propose to replace the InfoNCE loss with semantic matching loss based on medical knowledge to eliminate false negatives in contrastive learning. We prove that MedCLIP is a simple yet effective framework: it outperforms state-of-the-art methods on zero-shot prediction, supervised classification, and image-text retrieval. Surprisingly, we observe that with only 20K pre-training data, MedCLIP wins over the state-of-the-art method (using 200K data). The code is available at https://github.com/RyanWangZf/MedCLIP.
%R 10.18653/v1/2022.emnlp-main.256
%U https://aclanthology.org/2022.emnlp-main.256/
%U https://doi.org/10.18653/v1/2022.emnlp-main.256
%P 3876-3887

Markdown (Informal)

[MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://aclanthology.org/2022.emnlp-main.256/) (Wang et al., EMNLP 2022)

• MedCLIP: Contrastive Learning from Unpaired Medical Images and Text (Wang et al., EMNLP 2022)

ACL

• Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, and Jimeng Sun. 2022. MedCLIP: Contrastive Learning from Unpaired Medical Images and Text. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3876–3887, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.