The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Dynamic Graph Enhanced Contrastive Learning for Chest X-Ray Report Generation
Open accessAbstract
Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes to facilitate chest X-ray report generation with Contrastive Learning, named DCL. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Then we explore specific knowledge extracted from the retrieved reports to add additional nodes or redefine their relations in a bottom-up manner. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation. Finally, this paper introduces Image-Report Contrastive and Image-Report Matching losses to better represent visual features and textual information. Evaluated on IU-Xray and MIMIC-CXR datasets, our DCL outperforms previous state-of-the-art models on these two benchmarks.
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“It is not possible to quantitatively compare to models trained to generate other sections, such as Impression 17 or both Findings and Impression together.15,58”
From MAIRA-2: Grounded Radiology Report Generation (2024) · cites this paper as [15]
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Sorted by citations
Evaluating progress in automatic chest X-ray radiology report generation
Yu, F., Endo, M., Krishnan, R., Pan, I., Tsai, A., Reis, E. P., Fonseca, E. K. U. N., Lee, H. M. H., Abad, Z. S. H., Ng, A. Y., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100802 · Cited by 200
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Multimodal generative AI for medical image interpretation
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Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey
Lin, Q., Zhu, Y., Mei, X., Huang, L., Ma, J., He, K., Peng, Z., Cambria, E., & Feng, M. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102795 · Cited by 86
Metadata
- Authors
- Li, M., Lin, B., Chen, Z., Lin, H., Liang, X., & Chang, X.
- Affiliations
- University of Technology Sydney (AU)
- Sun Yat-sen University (CN)
- University of Hong Kong (HK)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 3334-3343
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076127
- Cited by
- 206
- References
- 45
- Citations by year
- 2026: 52 · 2025: 86 · 2024: 54 · 2023: 14
- FWCI
- 22.03
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Key R&D Program of China (2020AAA0109700)
- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- chest X-ray report generation, contrastive learning, dynamic knowledge graphs, medical knowledge graph, MIMIC-CXR
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