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
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
The cited source says what the claim attributes to it.
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
The cited source says what the claim attributes to it.
In the citing paper
“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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Cited by
Sorted by citations
Items 101–151 of 151
Zhong, X., Batmanghelich, K. N., & Sun, L. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00097 · Cited by 2
MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks
Hirsch, E., Dawidowicz, G., & Tal, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73209-6_2 · Cited by 2
Dual-level semantic collaboration and inference network for medical image report generation
Zhang, J., Liu, Y., Shao, M., Chen, C., Wang, Z., Li, J., Wan, Y., & Yu, P. S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114278 · Cited by 2
Nishino, T. (2023) · Journal of Natural Language Processing · doi:10.5715/jnlp.30.250 · Cited by 2
Cardenas, R., Yao, B., Wang, D., & Hou, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.76 · Cited by 4
Optimizing Medical Image Report Generation with Varied Attention Mechanisms
Reddy, P. M., Verma, V. K., & Varma, M. V. C. (2023) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i59117.2023.10398149 · Cited by 2
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Trienes, J., Youssef, P., Schlötterer, J., & Seifert, C. (2023) · International Conference on Natural Language Generation · doi:10.18653/v1/2023.inlg-main.13 · Cited by 3
Adversarial Training with Comprehensive Objective for Medical Image Report Generation
Zhang, X., & Liu, J. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385588 · Cited by 1
Exploring Vision Language Pretraining with Knowledge Enhancement via Large Language Model
Tung, C., Lin, Y., Yin, J., Ye, Q., & Chen, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-67751-9_7 · Cited by 1
Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components
Warr, H., Ibrahim, Y., McGowan, D. R., & Kamnitsas, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73158-7_7 · Cited by 1
RDLoRA:Rank-Decomposed Low-Rank Adaptation for Multi-task Medical Applications
He, Q., & Le, S. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_29 · Cited by 1
Adapting Vision Language Models for Structured Clinical Description Generation in Mammography
Castro, A. C., Rodrigues, D., & Filho, A. R. G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05559-0_24 · Cited by 1
Automated Radiology Report Generation Based on Topic-Keyword Semantic Guidance
Xiao, J., Liu, H., Dong, R., Liu, J., & Yu, H. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209777 · Cited by 1
Review of generative AI for lesion localization and automatic report generation
Sun, K., Wang, Y., Huang, S., Liu, Y., Chen, H., Lian, Z., Tan, M., Lin, X., Zhou, W., Qiu, Z., et al. (2026) · Med-X · doi:10.1007/s44258-026-00076-8 · Cited by 1
Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.05201 · Cited by 49
Tang, W., Pei, C., Yu, P., Zhang, H., Min, X., Chen, C., Han, K., Xu, W., & Zhang, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47401-9_8 · Cited by 1
Yan, S., Yin, H., Tsang, I. W.-H., & Cheung, W. K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73158-7_4 · Cited by 1
Khan, A. M., Mohsan, M. M., Akram, M. U., Hassan, T., Khawaja, S. G., & Qayyum, A. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108340 · Cited by 1
A Textual Dataset for Situated Proactive Response Selection
Otani, N., Araki, J., Kim, H., & Hovy, E. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.214 · Cited by 1
Abdaoui, H., Barbaria, S., Al Kuwaiti, A., Raouafi, N., Ben Salah, R., & Boussi Rahmouni, H. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2026.1875905
Pino, P., Parra, D., Dunstan, J., & Besa, C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4052411
Multimodal Representation Learning For Real-World Applications
Joshi, A. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536221.3557030
Adaptively Multi-Objective Adversarial Training for Medical Image Report Generation
Zhang, X., & Liu, J. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10386004
Tubaishat, A., Zia, T., Windridge, D., Nawaz, M., & Razzaq, S. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10640-1
GIT-CXR: End-to-End Transformer for Chest X-Ray Report GenerationPreprint
Sîrbu, I., Sîrbu, I.-R., Bogojeska, J., & Rebedea, T. E. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2501.02598 · Cited by 2
Chen, B., Liu, G., & Zhang, Q. (2025) · Physiological Measurement · doi:10.1088/1361-6579/ada869
Disease probability-enhanced follow-up chest X-ray radiology report summary generation
Wang, Z., Deng, Q., So, T. Y., Chiu, K. W.-H., Lee, K., & Hui, E. S. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-12684-2
Liu, J., Deng, Y., Liu, J., Song, D., Duan, X., & Li, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108553
MFDP: Multi-View Feature Integration and Enhanced Disease Prompting for Radiology Report Generation
Zhao, Y., Yang, K., Wu, Y., & Li, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3632356 · Cited by 1
RSTA: A Recurrent Scene Topic-Aware Model for Multi-Scene Endoscopic Report Generation
Yuan, X., Kuang, J., Hua, L., Zhao, G., Zhang, C., & Li, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227409
Multi-modal Medical Image Captioning for Enhanced Diagnostic Interpretation
Lee, D. T. Y., & Hoo, W. L. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-08243-5_10
Tang, M., Ding, W., Wei, C. A. I., & Jiang, Y. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00475-2
Shyni, I. D., Manish Dutt, S., & Kabilesh Kumar, R. (2025) · International Conference on Intelligent Computing, Information and Control Systems (ICOIICS) · doi:10.1109/icoiics67115.2025.11389838
Case2Knowledge: Structured Knowledge-Infused Prompting for Faithful Radiology Report Generation
Gao, Q., Liu, T., Sun, Z., Wang, B., Yin, B., & Liu, Z. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464942
Wang, S., Pang, Z., Wang, S., & Xu, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462928
Bouridane, A., Hezil, N., Hamoudi, R., Al-Maadeed, S. A., & Rogov, O. Y. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515574
REALM: Reinforcement Learning-Based Cross-Modal Alignment for Fine-Grained Medical Report Generation
Teng, P., Liu, W., Wang, Y., Yang, H., & Xing, M. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-7087-4_12
A Transformer-Based Cross-Modal Framework for Clinically Faithful Medical Image Captioning
Shankar, M., Manikandan, R., Kambam, B., Nayunigari, D., Kuruba, B., & Upputholla, H. (2026) · International Conference on Signal, Systems, and Computing for Next-Gen Automation (ICSSCNA) · doi:10.1109/icsscna68616.2026.11546907
Automated Chest X-Ray Report Generation Using Vision-Language Models: a Llama 3.2 Based Approch
Arman, M., Shovon, R. B., Emon, S. B., Sameha, J. A., Faisal, F. S., & Sayem, M. R. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11545770
Prior-Anchored Debiasing for Long-Tailed Multi-Organ Pathology Report Generation
Yang, F., Liu, J., Pang, Y., Chen, P., Lyu, X., Wang, S., Leung, H., & Chen, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38224-5_39
IRG-DR: Interactive Report Generation Framework with Evidence Guidance for Diabetic Retinopathy
Zhang, X., Li, X., Yuan, X., He, P., Wang, M., & Wang, J. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582554
X-CapFusion: Efficient label-aware model for medical image captioning
Amorim, M. T., Oliveira, H. S., Oliveira, H. P., & Teixeira, L. F. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111028
TRACE: Temporal Radiology with Anatomical Change Explanation for Grounded X-Ray Report Generation
Aranya, O. F. M. R. R., & Desai, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31933-3_7 · Cited by 3
Risk Aware Prompt-Guided CNN–LLM Framework for Brain MRI Diagnostic Report GenerationPreprint
Senthamilselvi, M., & Palanisamy, K. (2026) · Research Square · doi:10.21203/rs.3.rs-10802065/v1
Prakash, D., Kulkarni, P., Rangarajan, V., & Kumar, M. (2026) · Indian Journal of Nuclear Medicine · doi:10.25259/ijnm_104_2026
Rahman, H., Mahmood, T., & Raza, A. (2026) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/u145014357
Xiao, Z., Liao, Z., Lu, Z., Hu, S., Leng, S., Zhang, J., & Xia, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_48
Xiong, L., Zhang, Y., Song, H., Peng, Q., & Cui, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38062-3_55
Self-citationAl-Qahtani, Y., Al-Salem, F., & Al-Harbi, A. (2024) · Journal of Health Informatics and Digital Systems · doi:10.68159/o358775030
RadLens: Radiology Report Generation Using a Vision Transformer and Retrieval-Augmented Generation
Nithianandan, P. N., Tiwari, J., Davarpanah, S., & Norouzifard, M. (2026) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr70517.2026.00081
Souayah, N. (2026) · Fractal and Fractional · doi:10.3390/fractalfract10100717
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
