A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
Open accessAbstract
Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical visual question answering, clinical image-text retrieval, clinical report auto-generation. In this study, we adopt four pre-trained V+L models: LXMERT, VisualBERT, UNIER and PixelBERT to learn multimodal representation from MIMIC-CXR images and associated reports. External evaluation using the OpenI dataset shows that the joint embedding learned by pre-trained V+L models demonstrates performance improvement of 1.4% in thoracic finding classification tasks compared to a pioneering CNN+RNN model. Ablation studies are conducted to further analyze the contribution of certain model components and validate the advantage of joint embedding over text-only embedding. Attention maps are also visualized to illustrate the attention mechanism of V+L models.
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More modalities are emerging beyond vision, text, and audio.
“More modalities.In addition to the general modalities of vision, text, and audio, further diverse modalities are emerging, e.g., Pano-AVQA [144] -the first large-scale spatial and audio-visual question answering dataset on 360 • videos, YouTube-360 (YT-360) [145] (360 • videos), AIST++ [146] (a new multimodal dataset of 3D dance motion and music), Artemis [147] (affective language for visual arts)”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [146]
A common practice for hierarchical attention (multi-stream to one-stream) is independent Transformer streams whose outputs are concatenated and fused by another Transformer.
“A common practice is that multimodal inputs are encoded by independent Transformer streams and their outputs are concatenated and fused by another Transformer [146]”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [146]
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- Authors
- Li, Y., Wang, H., & Luo, Y.
- Affiliations
- Northwestern University (US)
- Venue
- 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
- Publisher
- IEEE
- Published
- 2020-12-16
- Pages
- 1999-2004
- OpenAlex
- W3128510757
- Cited by
- 81
- References
- 29
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 16 · 2023: 24 · 2022: 16 · 2021: 7 · 2020: 3
- FWCI
- 3.85
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- vision-language models, multimodal representation learning, pre-trained models, LXMERT, chest X-ray reports, MIMIC-CXR, attention maps, medical report generation
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