The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
On the Automatic Generation of Medical Imaging Reports
Open accessAbstract
Medical imaging is widely used in clinical practice for diagnosis and treatment.Report-writing can be error-prone for unexperienced physicians, and timeconsuming and tedious for experienced physicians.To address these issues, we study the automatic generation of medical imaging reports.This task presents several challenges.First, a complete report contains multiple heterogeneous forms of information, including findings and tags.Second, abnormal regions in medical images are difficult to identify.Third, the reports are typically long, containing multiple sentences.To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the prediction of tags and the generation of paragraphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs.We demonstrate the effectiveness of the proposed methods on two publicly available datasets.
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“Recently, deep learning methods have also been used for automated generation of radiology reports , typically incorporating long-short-term-memory (LSTM) network models to generate the textual paragraphs [314, 315, 316, 317], and also to identify findings in radiology reports [318, 319, 320].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [314]
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 5×
“Results in Table 3 show that Ours-CoAttention and VGG-19 [17] network performs very similar for tag prediction.”
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“than adopting a single-layer LSTM (Hochreiter and Schmidhuber, 1997), which is less capable of modeling long word sequences, we leverage the compositional nature of the report and adopt a hierarchical LSTM to produce long texts.”
MethodAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 1×
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MethodBLEU: a method for automatic evaluation of machine translation
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Show and tell: A neural image caption generator
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“Note that the only difference between Ours-NoAttention and CNN-RNN [18] in Table 1 is that Ours-NoAttention adopts a hierarchical LSTM decoder while CNNRNN [18] only adopts a single-layer LSTM.”
BackgroundMethodInfluentialLong-term recurrent convolutional networks for visual recognition and description
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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 · cited 2×
“We report the paragraph generation (upper part of Table 1) and one sentence generation (lower part of Table 1) results using the following captioning evaluation tools: BLEU [13], METEOR [4], ROUGE [12] and CIDER [17].”
MethodMeteor Universal: Language Specific Translation Evaluation for Any Target Language
Denkowski, M., & Lavie, A. (2014) · WMT@ACL · doi:10.3115/v1/w14-3348 · Cited by 2,108 · cited 2×
“We report the paragraph generation (upper part of Table 1) and one sentence generation (lower part of Table 1) results using the following captioning evaluation tools: BLEU [13], METEOR [4], ROUGE [12] and CIDER [17].”
MethodImage Captioning with Semantic Attention
You, Q., Jin, H., Wang, Z., Chen, F., & Luo, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.503 · Cited by 1,869 · cited 6×
“We first compare our method with several state-of-the-art image captioning models: CNN-RNN [19], LRCN [5], Soft ATT [20], ATT-RK [21].”
BackgroundMethodInfluential
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Tabatabaee, S., & Khasawneh, M. T. (2026) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2026.2674247
Real-World Applications of Multimodal Knowledge Systems
Zheng, C., & Li, Q. (2026) · Wireless networks · doi:10.1007/978-3-032-18761-1_7
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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
Gupta, D., & Dawn, S. (2026) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-026-03345-2
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Chen, L., Jin, R., Luo, J., Chen, H., Strotzer, Q., Yan, R., Xue, Y., Prevedello, L. M. S., & Wu, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_41
Zhang, X., Zhao, J., Ruan, W., & Li, X. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582171
“Jing et al. [3] pioneered a co-attention mechanism using a hierarchical LSTM [13] to model topics and descriptions separately.”
ZPRRF: Zero-Shot Prior and Rule-Guided Radiology Reporting Framework
Huang, B., Zhao, G., Li, Y., Zhao, X., Luo, Y., & Zhang, K. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3495-0_35
Peng, Q., Cui, S., Deng, M., & Xiong, L. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3495-0_19
Comparative Analysis of CNN-LSTM Architectures to Grade Human Blastocysts
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“In medical imaging, this paradigm has been effectively adapted to generate descriptive text from visual data, such as automated radiology report generation from X-rays or MRIs [13, 14].”
Semantic Bias-Aware Medical Report Generation for Brain CT Imaging
Xiang, X., Yao, X., & Chen, P. (2026) · Multidimensional Signal Processing Workshop · doi:10.1109/ivmsp68335.2026.11618762
“Consequently, automatic medical report generation has attracted growing attention as a practical tool for assisting radiologists and improving clinical efficiency [4], [5].”
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
RadReport-GNN: A Graph-Based Multimodal Fusion Architecture for Automated Medical Report Generation
Safari, F., & Mohammadi, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-24810-7_21
Rohilla, H., Singh, A. P., & Ratna, S. (2026) · International Conference on Data Science for Cyber-Physical Systems Resilience using Advanced Applications (ICDCA) · doi:10.1109/icdca69396.2026.11620238
“Early systems used RNNs with LSTM/GRU cells and attention mechanisms to focus on salient image regions [8], [9], [26], [27].”
Dual-View Vision Transformer with Cross-Modal Memory for Radiology Report Generation
Lamred, N., Ayed, N. G. B., & Charfi, N. (2026) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit70676.2026.11631055
“[ 10], [11].”
Aderinto, P. V., & Ghaziasgar, M. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-33957-7_15
Kushwah, V. R. S., & Shrivastava, A. K. (2026) · International Conference on Emerging Trends in Information, Communication & Systems (ICETICS) · doi:10.1109/icetics69505.2026.11651322
“Because of this, reports written by different doctors may vary [2][3].”
InfluentialCAM-Guided Radiology Report Generation
Lyu, M. (2026) · International Conference on Bioinformatics and Intelligent Computing (BIC) · doi:10.1145/3809986.3810099
“Jing et al. [5] introduced a co-attention model that attends to both image regions and report text, while Li et al. [7] combined retrieval and generation for report writing.”
Multi-guided and pixel enhancement model for radiology report generation
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“Therefore, as a potential auxiliary or alternative solution, Radiology Report Generation (RRG) is receiving increasing attention from researchers [10, 12, 24, 33].”
Rahman, H., Mahmood, T., & Raza, A. (2026) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/u145014357
Khalid, O., Nadeem, S., Farooq, B., & Saeed, H. (2024) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/s049146327
Huang, J.-L., Zhu, J., & Ni, X. (2026) · Journal of Medical Internet Research · doi:10.2196/97007
“Earlier medical imaging report generation research predated the current large language model (LLM) wave and relied on neural architectures designed for specific text generation tasks rather than general purpose foundation models [2,3].”
Cross-Domain Chest Radiograph Report Generation Under Dataset Shift: Bidirectional Audit Study
Sun, Y., Jiang, J. J., & Chen, Y. (2026) · JMIR Medical Informatics · doi:10.2196/95888
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
CARE: Clinically Aligned Retrieval Evidence for Consistent Radiology Report Generation
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Interpretable Radiology Report Generation via Prototype-Guided Contrastive Learning
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Wavelet-Enhanced Coarse-to-Fine Radiology Report Generation with Large Language Models
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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
“The task of automated radiology report generation was formalized by Jing et al. [2], who proposed a co-attention CNN-RNN framework with a hierarchical LSTM network for multi-sentence report generation.”
Hybrid Transformer-Based Multimodal Fusion for Chest X-ray Report Generation
Chau, V. V., Nguyen, T. M., & Meesad, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-28048-0_14
Metadata
- Authors
- Jing, B., Xie, P., & Xing, E.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 2577-2586
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2770165365
- Cited by
- 657
- References
- 28
- Citations by year
- 2026: 57 · 2025: 123 · 2024: 115 · 2023: 111 · 2022: 80 · 2021: 81 · 2020: 45 · 2019: 34 · 2018: 11
- FWCI
- 12.69
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- multi-task learning, co-attention, hierarchical LSTM, tag prediction, clinical report writing
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