The DOI matches its Crossref record; 52 of the 55 references it cites were found in a registry or an index.
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
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Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, Dan Jurafsky. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
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“This model also reports the best scores on the NLG metrics as well as improvements of +7.59% on factENT and +14.2% on factENTNLI over Miura et al. (2021).”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“So far, these new methods rely on weakly-supervised approaches (rule-based) to construct NLI models for radiology reports and biomedical named entity recognition systems (Zhang et al., 2021) that are not specific to chest X-rays.”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“rewards based solely on entities (Miura et al., 2021) or entities and relations cannot be optimized without counter-effects happening.”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Miura et al. (2021) have shown that existing systems are inadequate in factual completeness and consistency”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
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In the citing paper
“On MIMIC-CXR, our baseline is up 2.3% on factENT and up 8.0% on factENTNLI compared to Miura et al. (2021).”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
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“Despite these”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
The cited source says what the claim attributes to it.
In the citing paper
“and that an image-to-text radiology report generation (RRG) system can be substantially improved by replacing the widely used NLG metrics with”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
The cited source says what the claim attributes to it.
In the citing paper
“Secondly, these systems that achieve high performance on NLG metrics are not always factually complete or consistent due to both inadequate training and evaluation of these systems.”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
The cited source says what the claim attributes to it.
In the citing paper
“In contrast, the generation of complete narrative radiology reports11,12,13,14,15,16,17,18,19,20,21 moves past that simplification and is consistent with how radiologists communicate diagnostic information: the narrative report allows for highly diverse and nuanced findings, including association of findings with anatomic location, and expressions of uncertainty.”
From Evaluating progress in automatic chest X-ray radiology report generation (2023) · cites this paper as [11]
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In the citing paper
“However, their customized transformer model required 7 days of training on a single NVIDIA A100 GPU, whereas our BrainGPT model requires only 12 h of fine-tuning on two NVIDIA A100 GPUs.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [36]
Experimental — accuracy not yet measuredIn the citing paper
“Recent work by Hamamci et al. revealed that generative models are competent in 3D chest CT report generation at the state-of-the-art (SOTA) level (BLEU-1 = 46, BLEU-4 = 36.9, METEOR = 29.5, ROUGE-L = 45.9)35–37.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [36]
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Citations
Cites
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
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
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
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
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015
MIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141
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On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
Self-citationTransformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,523
Towards Generalist Biomedical AI
Tu, T., Azizi, S., Driess, D., Schaekermann, M., Amin, M., Chang, P.-C., Carroll, A., Lau, C., Tanno, R., Ktena, I., et al. (2024) · NEJM AI · doi:10.1056/aioa2300138 · Cited by 483
Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing
Boecking, B., Usuyama, N., Bannur, S., Castro, D. C., Schwaighofer, A., Hyland, S., Wetscherek, M., Naumann, T., Nori, A., Alvarez-Valle, J., et al. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20059-5_1 · Cited by 317
“[53] Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, and Dan Jurafsky.”
BackgroundMethodResultInfluentialNeural Natural Language Processing for unstructured data in electronic health records: A review
Li, I., Pan, J. N., Goldwasser, J., Verma, N., Wong, W. P., Nuzumlalı, M. Y., Rosand, B., Li, Y., Zhang, M. M., Chang, D., et al. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100511 · Cited by 280
Metadata
- Authors
- Miura, Y., Zhang, Y., Tsai, E., Langlotz, C., & Jurafsky, D.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2021
- Pages
- 5288-5304
- OpenAlex
- W3166845084
- Cited by
- 149
- References
- 55
- Citations by year
- 2026: 25 · 2025: 31 · 2024: 35 · 2023: 36 · 2022: 21 · 2021: 1
- FWCI
- 7.59
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, 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
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Keywords
- image-to-text generation, radiology report generation, factual consistency, medical report summarization
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