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 MAIRA-2 to models trained to generate other sections, such as Impression or both Findings and Impression together.
“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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Granularity Matters: Pathological Graph-driven Cross-modal Alignment for Brain CT Report Generation
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ChEX: Interactive Localization and Region Description in Chest X-Rays
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Act Like a Radiologist: Radiology Report Generation Across Anatomical Regions
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Self-citationAligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting
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DFMC: Feature-Driven Data-Free Knowledge Distillation
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Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-Ray Report Generation
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Medical Report Generation via Multimodal Spatio-Temporal Fusion
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Multifocal region-assisted cross-modality learning for chest X-ray report generation
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Differential-Perceptive and Retrieval-Augmented MLLM for Change Captioning
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Topicwise Separable Sentence Retrieval for Medical Report Generation
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Enhanced Knowledge Injection for Radiology Report Generation
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Interactive dual-stream contrastive learning for radiology report generation
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A multi-task learning model for clinically interpretable sesamoiditis grading
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Abnormal-region-aware Multi-modal Feature Fusion for medical report generation
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Contrastive Knowledge-Guided Large Language Models for Medical Report Generation
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EfficienTransNet: An Automated Chest X-ray Report Generation Paradigm
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Dual Graph Convolutional Networks for Chest X-ray Report Generation
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Symbiotic Fusion of Evolving Anatomical Landscapes for Intelligent Diagnostic Narratives
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Semantic Feedback-Based RAG for Radiology Report Generation
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R2GenCSR: Mining Contextual and Residual Information for LLMs-Based Radiology Report Generation
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A Doctors Behavior Aware and Domain Knowledge Driven Model for Medical Report Generation
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The Long-Term Memory Transformer with Multimodal Fusion for Radiology Report Generation
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Textual Inversion and Self-supervised Refinement for Radiology Report Generation
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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