Automatic Medical Image Report Generation with Multi-view and Multi-modal Attention Mechanism
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Generated diagnostic reports must focus on normal and abnormal findings related to medical characteristics rather than general descriptions, and high precision is mandatory.
“Moreover, high precision is mandatory, and generated reports must focus on normal and abnormal findings related to medical characteristics rather than general descriptions [45,46].”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [45]
Diagnostic report generation is time-consuming and knowledge-intensive.
“In general, diagnostic report generation is a time-consuming and knowledge-intensive task [45].”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [45]
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 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
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 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
(2004) · Cited by 9,718 · see citing works
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 · see citing works
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 · see citing works
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319 · see citing works
Self-citationLong-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · see citing works
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2018) · 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.2018.00636 · Cited by 5,117 · see citing works
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902 · see citing works
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 · see citing works
Self-Critical Sequence Training for Image Captioning
Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (2017) · 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.2017.131 · Cited by 2,177 · see citing works
Image 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 1,859 · see citing works
Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning
Lu, J., Xiong, C., Parikh, D., & Socher, R. (2017) · 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.2017.345 · Cited by 1,724 · see citing works
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Bengio, S., Vinyals, O., Jaitly, N., & Shazeer, N. (2015) · arXiv · doi:10.48550/arxiv.1506.03099 · Cited by 1,621 · see citing works
From captions to visual concepts and back
Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015) · 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.2015.7298754 · Cited by 1,305 · see citing works
Preparing a collection of radiology examinations for distribution and retrieval
Demner-Fushman, D., Kohli, M. D., Rosenman, M. B., Shooshan, S. E., Rodriguez, L., Antani, S., Thoma, G. R., & McDonald, C. J. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv080 · Cited by 1,164 · see citing works
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)Preprint
Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., & Yuille, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6632 · Cited by 1,039 · see citing works
On the Automatic Generation of Medical Imaging Reports
Jing, B., Xie, P., & Xing, E. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1240 · Cited by 657 · see citing works
Sequence Level Training with Recurrent Neural Networks
Ranzato, M., Chopra, S., Auli, M., & Zaremba, W. (2016) · International Conference on Learning Representations · Cited by 572 · see citing works
An Actor-Critic Algorithm for Sequence Prediction
Bahdanau, D., Brakel, P., Xu, K., Goyal, A., Lowe, R., Pineau, J., Courville, A., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1607.07086 · Cited by 423 · see citing works
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · see citing works
Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Li, C. Y., Liang, X., Hu, Z., & Xing, E. P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016666 · Cited by 316 · see citing works
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
Li, C. Y., Liang, X., Hu, Z., & Xing, E. P. (2018) · arXiv · doi:10.48550/arxiv.1805.08298 · Cited by 271 · see citing works
Multimodal Recurrent Model with Attention for Automated Radiology Report Generation
Xue, Y., Xu, T., Long, L. R., Xue, Z., Antani, S., Thoma, G. R., & Huang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_52 · Cited by 241 · see citing works
Yuan, J., Liao, H., Luo, R., & Luo, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_80 · Cited by 215 · see citing works
Discrepancy and error in radiology: concepts, causes and consequences.
Brady, A. P., Laoide, R. Ó., McCarthy, P., & McDermott, R. (2012) · Europe PMC (PubMed Central) · Cited by 204 · see citing works
Liu, F., Ren, X., Liu, Y., Wang, H., & Sun, X. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1013 · Cited by 69 · see citing works
Connecting the Dots Between MLE and RL for Sequence GenerationPreprint
Tan, B., Hu, Z., Yang, Z., Salakhutdinov, R., & Xing, E. P. (2019) · arXiv (Cornell University) · Cited by 13 · see citing works
Flagged first, then by citations
3 of the 35 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328 · see citing works
Survey on Explainable AI: From Approaches, Limitations and Applications Aspects
Yang, W., Wei, Y.-C., Wei, H., Chen, Y., Huang, G., Li, X., Li, R., Yao, N., Wang, X., Gu, X., et al. (2023) · Human-Centric Intelligent Systems · doi:10.1007/s44230-023-00038-y · Cited by 275 · see citing works
Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110786 · Cited by 233 · see citing works
Contrastive Attention for Automatic Chest X-ray Report Generation
Liu, F., Yin, C., Wu, X., Ge, S., Zhang, P., & Sun, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.23 · Cited by 175 · see citing works
Automatic captioning for medical imaging (MIC): a rapid review of literature
Beddiar, D. R., Oussalah, M., & Seppänen, T. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10270-w · Cited by 103 · see citing works
Deep image captioning: A review of methods, trends and future challenges
Xu, L., Tang, Q., Lv, J., Zheng, B., Zeng, X., & Li, W. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126287 · Cited by 78 · see citing works
Multi-modal transformer architecture for medical image analysis and automated report generation
Raminedi, S., Shridevi, S., & Won, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-69981-5 · Cited by 74 · see citing works
Mir, A. N., & Rizvi, D. R. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111413 · Cited by 34 · see citing works
Contrastive Attention for Automatic Chest X-ray Report GenerationPreprint
Liu, F., Yin, C., Wu, X., Ge, S., Zou, Y., Zhang, P., Zou, Y., & Sun, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06965 · Cited by 36 · see citing works
Radiology report generation using transformers conditioned with non-imaging data
Aksoy, N., Ravikumar, N., & Frangi, A. F. (2023) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2653672 · Cited by 17 · see citing works
MAIRA-2: Grounded Radiology Report Generation
Bannur, S., Bouzid, K., Castro, D. C., Schwaighofer, A., Thieme, A., Bond-Taylor, S., Ilse, M., Pérez-García, F., Salvatelli, V., Sharma, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2406.04449 · Cited by 19 · see citing works
“Incorporating the lateral view has been demonstrated to improve automated report generation (Liu et al., 2024; Lee et al., 2023; Mondal et al., 2023; Yang et al., 2020; Yuan et al., 2019).”
BackgroundA vision attention driven Language framework for medical report generation
Arısoy, M. V., Arısoy, A., & Uysal, İ. (2025) · Scientific Reports · doi:10.1038/s41598-025-95666-8 · Cited by 15 · see citing works
MvKeTR: Chest CT Report Generation With Multi-View Perception and Knowledge Enhancement
Deng, X., He, X., Bao, J., Zhou, Y., Cai, S., Cai, C., & Chen, Z. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3581289 · Cited by 6 · see citing works
Review on approaches to concept detection in medical images
Miranda, D., Thenkanidiyoor, V., & Dinesh, D. A. (2022) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2022.02.012 · Cited by 3 · see citing works
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 · see citing works
Generating Caption From Images Using Flickr Image Dataset
Sharma, P., Kaur, K., Gandotra, A., Patial, P., Radhika, & Gaba, S. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724963 · Cited by 1 · see citing works
Zhu, J., & Lü, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98688-8_14
Hamma, M. B., Merrouni, Z. A., Frikh, B., & Ouhbi, B. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11556-z
- Authors
- Yang, S., Niu, J., Wu, J., & Liu, X.
- Affiliations
- State Key Laboratory of Virtual Reality Technology and Systems (CN)
- Beihang University (CN)
- Corresponding author
- Niu, Jianwei
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 687-699
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3090105394
- Cited by
- 17
- References
- 35
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 5 · 2022: 3 · 2021: 2
- FWCI
- 0.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- 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
- medical report generation, radiology report writing, multimodal attention, medical concept extraction, visual feature extraction
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