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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The RGER model reports the best scores on NLG metrics as well as improvements of +7.59% on factENT and +14.2% on factENTNLI over Miura et al. (2021).
“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
Split into 2 atoms — 1 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredNew factually-oriented methods rely on weakly-supervised approaches (rule-based) to construct NLI models for radiology reports and biomedical named entity recognition systems that are not specific to chest X-rays.
“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
Section 3.3 says the authors use weakly supervised rules to train a radiology-report NLI model and use Stanza’s clinical models for named entity recognition. Section 3.2.1 likewise identifies Stanza as the recognizer used for radiology reports. The paper does not establish the broader claim that new factually oriented methods generally rely on these approaches, or explicitly state that the recognizer is not specific to chest X-rays. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRewards based solely on entities or on entities and relations cannot be optimized without counter-effects occurring.
“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
The Generated Report passage and §3.2.2 say that adding NLI controls overestimation of disease when optimizing the entity-match reward, supporting a counter-effect for entity-based optimization. They do not establish the categorical claim that such rewards cannot be optimized without counter-effects. The citation is attached to entity-based rewards, not the separate reference to entities and relations. (LLM, conf=0.84)
Experimental — accuracy not yet measuredExisting image-to-text radiology report generation systems are inadequate in factual completeness and consistency.
“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
On MIMIC-CXR, the NLL baseline is up 2.3% on factENT and up 8.0% on factENTNLI compared to Miura et al. (2021).
“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
Despite being weakly supervised or limited to generic biomedical entities, the use of factually-oriented methods showed substantial improvements on a wide range of metrics and board-certified radiologists' evaluations.
“Despite these”
From Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022) · cites this paper as Miura et al., 2021
An image-to-text radiology report generation system can be substantially improved by replacing widely used NLG metrics with factually-oriented methods encouraging the generation of domain entities consistent with the reference and describing these entities in inferentially consistent ways.
“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
Current radiology report generation systems that achieve high performance on NLG metrics are not always factually complete or consistent due to inadequate training and evaluation.
“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 generation of complete narrative radiology reports moves past that simplification and is consistent with how radiologists communicate diagnostic information.
“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]
Hamamci et al.'s customized transformer model required 7 days of training on a single NVIDIA A100 GPU.
“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]
Generative models are competent in 3D chest CT report generation at the state-of-the-art level (BLEU-1 = 46, BLEU-4 = 36.9, METEOR = 29.5, ROUGE-L = 45.9).
“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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An Organ-Aware Diagnosis Framework for Radiology Report Generation
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BioSimCSE: BioMedical Sentence Embeddings using Contrastive learning
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“(Miura et al., 2021) proposed new metrics to evaluate the factual correctness and consistency of the generated report.”
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KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
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Recent Progress in Deep Learning for Chest X-Ray Report Generation
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Expert Insight-Enhanced Follow-Up Chest X-ray Summary Generation
Wang, Z., Lee, K., Deng, Q., So, T. Y., Chiu, K. W.-H., Zhou, B., & Hui, E. S. K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66535-6_21 · Cited by 2 · see citing works
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- image-to-text generation, radiology report generation, factual consistency, medical report summarization
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