Med-CXRGen-F Model Card

Task: Radiology Report Generation – Findings section (RRG Shared Task)

Paper and Resources

For details on Med-CXRGen-F, including its architecture, training strategy, and evaluation—please refer to the following resources:

• 📘 Paper: Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
• 💻 Code Repository: GitHub: Med-CXRGen

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How to Cite ✒️

If you use this model in academic or research contexts, please cite:

@inproceedings{zhang-etal-2024-gla,
    title = "Gla-{AI}4{B}io{M}ed at {RRG}24: Visual Instruction-tuned Adaptation for Radiology Report Generation",
    author = "Zhang, Xi  and
      Meng, Zaiqiao  and
      Lever, Jake  and
      Ho, Edmond S.L.",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.bionlp-1.54/",
    doi = "10.18653/v1/2024.bionlp-1.54",
    pages = "624--634",
}