The DOI matches its Crossref record; 46 of the 47 references it cites were found in a registry or an index.
Radiology report generation with a learned knowledge base and multi-modal alignment
Open accessAbstract
In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an automatic, multi-modal approach for report generation from a chest x-ray. Our approach, motivated by the observation that the descriptions in radiology reports are highly correlated with specific information of the x-ray images, features two distinct modules: (i) Learned knowledge base: To absorb the knowledge embedded in the radiology reports, we build a knowledge base that can automatically distill and restore medical knowledge from textual embedding without manual labor; (ii) Multi-modal alignment: to promote the semantic alignment among reports, disease labels, and images, we explicitly utilize textual embedding to guide the learning of the visual feature space. We evaluate the performance of the proposed model using metrics from both natural language generation and clinic efficacy on the public IU-Xray and MIMIC-CXR datasets. Our ablation study shows that each module contributes to improving the quality of generated reports. Furthermore, the assistance of both modules, our approach outperforms state-of-the-art methods over almost all the metrics. Code is available at https://github.com/LX-doctorAI1/M2KT.
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“the uncategorized keyword list in CheXpert may not be transferable from one image modality to different modalities”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [12]
In the citing paper
“the 14-feature retrieval rates cannot reflect the multi-semantic context (inclusive or exclusive; progression or resolution; relative location and size) of the disease nature over an extended clinical course.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [12]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
(2004) · Cited by 9,718
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664
Improved Baselines with Momentum Contrastive LearningPreprint
Chen, X., Fan, H., Girshick, R., & He, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04297 · Cited by 3,497
Cited by
Sorted by citations
METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens
Wang, Z., Liu, L., Wang, L., & Zhou, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01112 · Cited by 184
“Most recently, many works [16, 21, 22, 40, 41, 47] attempt to integrate knowledge graphs into medical report generation pipeline to improve the quality of the generated reports.”
BackgroundExplainable Deep Learning Methods in Medical Image Classification: A Survey
Patrício, C., Neves, J. C., & Teixeira, L. F. (2023) · ACM Computing Surveys · doi:10.1145/3625287 · Cited by 207
PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation
Jin, H., Che, H., Lin, Y., & Chen, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28038 · Cited by 181
Multimodal generative AI for medical image interpretation
Rao, V. M., Hla, M., Moor, M., Adithan, S., Kwak, S., Topol, E. J., & Rajpurkar, P. (2025) · Nature · doi:10.1038/s41586-025-08675-y · Cited by 134
Automated Radiology Report Generation: A Review of Recent Advances
Sloan, P., Clatworthy, P. L., Simpson, E., & Mirmehdi, M. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3408456 · Cited by 106
Metadata
- Authors
- Yang, S., Wu, X., Ge, S., Zheng, Z., Zhou, S. K., & Xiao, L.
- Affiliations
- Chinese Academy of Sciences (CN)
- Tencent (China) (CN)
- Beijing Tsinghua Chang Gung Hospital (CN)
- University of Science and Technology of China (CN)
- Beijing University of Posts and Telecommunications (CN)
- Corresponding author
- Xiao, Li
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2023-05
- Volume
- 86
- Pages
- 102798
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W4360604432
- Cited by
- 199
- References
- 47
- Citations by year
- 2026: 58 · 2025: 89 · 2024: 41 · 2023: 11
- FWCI
- 18.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Tencent
- National Natural Science Foundation of China (31900979)
- 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
- radiology report generation, chest radiography, knowledge distillation, multimodal alignment, text embeddings, semantic alignment, natural language generation
- MeSH terms
- Humans, Learning, Radiography, Radiology, Benchmarking, Knowledge Bases
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