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Cited by 207Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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 access
Li, Mingjie, Lin, Bingqian, Chen, Zicong, Lin, Haokun, Liang, Xiaodan, Chang, Xiaojun·2023·2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr52729.2023.00325
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Abstract

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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Crossref · 10.1109/cvpr52729.2023.00325

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Research works printed but not linked: 5

  1. huang, 0, Densely connected convolutional net-works, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700
  2. endo, 0, Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model, Machine Learning for Health, 209
  3. dosovitskiy, 2020, An image is worth 16×16 words: transformers for image recognition at scale, ArXiv Preprint
  4. johnson, 2019, and Steven Horng. Mimic-cxr: A large publicly available database of labeled chest radio-graphs, ArXiv Preprint
  5. song, 0, Cross-modal contrastive attention model for medical report generation, Proceedings of the 29th In-ternational Conference on Computational Linguistics, 2388

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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)
ORCID
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)
DOI
10.1109/cvpr52729.2023.00325
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
Licence
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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