ChestBioX-Gen: contextual biomedical report generation from chest x-ray images using BioGPT and co-attention mechanism
Open accessAbstract
Efficient and accurate radiology reporting is critical in modern healthcare for timely diagnosis and patient care. In this paper, we present a novel deep learning approach that leverages BioGPT and co-attention mechanisms for automatic chest X-ray report generation. Our model, termed “ChestBioX-Gen” is designed to bridge the gap between medical images and textual reports. BioGPT, a biological language model, contributes its contextual understanding to the task, while the co-attention mechanism efficiently aligns relevant regions of the image with textual descriptions. This collaborative combination enables ChestBioX-Gen to generate coherent and contextually accurate reports that embed complex medical findings. Our model not only reduces the burden on radiologists but also enhances the consistency and quality of reports. By automating the report generation process, ChestBioX-Gen contributes to faster diagnoses and improved patient care. Quantitative evaluations, measured through BLEU-N and Rouge-L metrics, demonstrate the model's proficiency in producing clinically relevant reports with scores of 0.6685, 0.6247, 0.5689, 0.4806, and 0.7742 on BLUE 1, 2, 3, 4, and Rouge-L, respectively. In conclusion, the integration of BioGPT and co-attention mechanisms in ChestBioX-Gen represents an advancement in AI-driven medical image analysis. As radiology reporting plays a critical role in healthcare, our model holds the potential to revolutionize how medical insights are extracted and communicated, ultimately benefiting both radiologists and patients.
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The growing gap between imaging demand and radiologist availability has intensified the challenge of radiology reporting, leading to delayed diagnoses and increased workloads across healthcare systems worldwide.
“The growing gap between imaging demand and radiologist availability has intensified this challenge, leading to delayed diagnoses and increased workloads across healthcare systems worldwide (1, 2).”
From Swin-Qwen3: a three-stage vision–language framework for automated radiology report generation with multi-agent verification (2026) · cites this paper as [2]
The Abstract (page unavailable) says timely radiology reporting matters for diagnosis and describes reducing radiologists’ burden and enabling faster diagnoses. It does not say that imaging demand is outpacing radiologist availability or that this gap has caused delayed diagnoses and increased workloads worldwide. (LLM, conf=0.78)
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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
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 · see citing works
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 · see citing works
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · 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
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 · 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
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616 · see citing works
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep LearningPreprint
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D. Y., Bagul, A., Langlotz, C. P., Shpanskaya, K. S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05225 · Cited by 2,283 · see citing works
Meteor 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 · see citing works
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2020.acl-demos.14 · Cited by 1,505 · see citing works
Hierarchical Question-Image Co-Attention for Visual Question AnsweringPreprint
Lu, J., Yang, J., Batra, D., & Parikh, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00061 · Cited by 1,303 · 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
BioGPT: generative pre-trained transformer for biomedical text generation and mining
Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., & Liu, T.-Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac409 · Cited by 1,150 · see citing works
PadChest: A large chest x-ray image dataset with multi-label annotated reports
Bustos, A., Pertusa, A., Salinas, J.-M., & de la Iglesia-Vayá, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101797 · Cited by 691 · see citing works
VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
Nguyen, H. Q., Lam, K., Le, L. T., Pham, H. H., Tran, D. Q., Nguyen, D. B., Le, D. D., Pham, C. M., Tong, H. T. T., Dinh, D. H., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01498-w · Cited by 435 · 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
Graph Neural Networks for Natural Language Processing: A Survey
Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000096 · Cited by 387 · see citing works
A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models
Zhang, H., Song, H., Li, S., Zhou, M., & Song, D. (2024) · ACM Computing Surveys · doi:10.1145/3617680 · Cited by 317 · see citing works
Dynamic Graph Enhanced Contrastive Learning for Chest X-Ray Report Generation
Li, M., Lin, B., Chen, Z., Lin, H., Liang, X., & Chang, X. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00325 · Cited by 206 · see citing works
Radiology report generation with a learned knowledge base and multi-modal alignment
Yang, S., Wu, X., Ge, S., Zheng, Z., Zhou, S. K., & Xiao, L. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102798 · Cited by 199 · 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
Learning to Summarize Radiology Findings
Zhang, Y., Ding, D. Y., Qian, T., Manning, C. D., & Langlotz, C. P. (2018) · arXiv · doi:10.48550/arxiv.1809.04698 · Cited by 126 · see citing works
Learning to Summarize Radiology Findings
Zhang, Y., Ding, D. Y., Qian, T., Manning, C. D., & Langlotz, C. P. (2018) · Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis · doi:10.18653/v1/w18-5623 · Cited by 126 · see citing works
Improving Medical X-ray Report Generation by Using Knowledge Graph
Zhang, D., Ren, A., Liang, J., Liu, Q., Wang, H., & Ma, Y. (2022) · Applied Sciences · doi:10.3390/app122111111 · Cited by 23 · see citing works
Kaur, N., & Mittal, A. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04454-z · Cited by 18 · see citing works
KGVL-BART: Knowledge Graph Augmented Visual Language BART for Radiology Report Generation
Kale, K., Bhattacharyya, P., Gune, M., Shetty, A., & Lawyer, R. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.246 · Cited by 15 · see citing works
Automated Report Generation: A GRU Based Method for Chest X-Rays
Akbar, W., Haq, M. I. U., Abdullah, A., Daudpota, S. M., Imran, A. S., & Ullah, M. (2023) · International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) · doi:10.1109/icomet57998.2023.10099311 · Cited by 13 · see citing works
Shetty, S., Ananthanarayana, V. S., & Mahale, A. R. (2023) · International Journal of Engineering · doi:10.5829/ije.2023.36.08b.16 · Cited by 10 · see citing works
Hierarchical decoding with latent context for image captioning
Zhang, J., Xie, Y., Li, K., Wang, Z., & Du, W. L. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07726-z · Cited by 9 · see citing works
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.07082 · cited 1×
“Qi et al. (2020), a Python natural language analysis package is used to extract anatomy and observation entities, and the graph is dynamically generated using insights from the top n T similar reports.”
Flagged first, then by citations
Research works printed but not linked: 1
- Liu, G., Hsu, T.-M. H., McDermott, M., Boag, W., Weng, W.-H., Szolovits, P., et al. (2019). "Clinically accurate chest X-ray report generation," in Machine Learning for Healthcare Conference (Beijing: PMLR), 249-269.
Cited by
Sorted by citations
Recent Progress in Deep Learning for Chest X-Ray Report Generation
Salhi, M., & Akhloufi, M. A. (2026) · BioMedInformatics · doi:10.3390/biomedinformatics6010003 · Cited by 2 · see citing works
Self-citationBouslimi, R. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102709 · Cited by 2 · see citing works
MedicalGLM: A Pediatric Medical Question Answering Model with a quality evaluation mechanism
Wang, X., Sun, Z., Wang, P., & Wei, B. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104793 · Cited by 3 · see citing works
Manonmani, S. P., & Malathi, S. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104348 · Cited by 1 · see citing works
Disease Classification and Report Generation from Chest X-Ray Images Using SigLIP and LLaVA
Kamalnath, M. S., Kumar, H., & Jiji, C. V. (2025) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon66050.2025.11375249 · Cited by 1 · see citing works
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
Automated Chest X-ray Captioning and Report Generation Using CheXNet and BioGPT
Mishra, D., Sai, K. P., Harshavardhan, K., Ram, C., & Reddy, K. G. (2025) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss65446.2025.11149939
PCOS-GPT: A RAG Enhanced LLM for Personalized Ultrasound Based PCOS Diagnosis
Venkatesh, M., Chitra, P., Dharini, A., Umayal, C. C., & Kiruthika, B. (2025) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn67655.2025.11368163
X-Plain: Vision-Language Knowledge Distillation for Interpretable Chest X-Ray Diagnosis
Sadhya, S., & Qi, X. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515681
Liu, X., Jin, Q., Zhang, T.-T., Tang, Z., Chen, Y., & Liu, Z. H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3699691
A hybrid parallel feature extractor using RegLeViT-Net for medical image report generation
Kumari, A. V. S. R., & Pappala, L. K. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110859
Srivastava, A. K., Garg, P., & Singh, A. (2026) · International Conference on Emerging VLSI and Semiconductor Technology for AI and Computing Applications (EVST) · doi:10.1109/evst69093.2026.11660634
- Authors
- Ouis, M. Y., & Akhloufi, M. A.
- Affiliations
- Université de Moncton (CA)
- Corresponding author
- Akhloufi, Moulay A.
- ORCID
- Akhloufi, Moulay A.
- Venue
- Frontiers in Imaging
- Publisher
- Frontiers Media SA
- Published
- 2024-04-19
- Volume
- 3
- ISSN
- 2813-3315 (ISSN-L)
- OpenAlex
- W4394961497
- Cited by
- 12
- References
- 29
- Citations by year
- 2026: 7 · 2025: 5
- FWCI
- 1.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Imaging (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Fondation de la recherche en santé du Nouveau-Brunswick
- New Brunswick Innovation Foundation (POF2021-006)
- Alliance de recherche numérique du Canada
- 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 › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- chest X-ray report generation, BioGPT, co-attention, image-text alignment, medical report generation
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