@inproceedings{aksoy-etal-2024-enhancing,
    title = "Enhancing Image-to-Text Generation in Radiology Reports through Cross-modal Multi-Task Learning",
    author = "Aksoy, Nurbanu  and
      Ravikumar, Nishant  and
      Sharoff, Serge",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.529",
    pages = "5977--5985",
    abstract = "Image-to-text generation involves automatically generating descriptive text from images and has applications in medical report generation. However, traditional approaches often exhibit a semantic gap between visual and textual information. In this paper, we propose a multi-task learning framework to leverage both visual and non-imaging data for generating radiology reports. Along with chest X-ray images, 10 additional features comprising numeric, binary, categorical, and text data were incorporated to create a unified representation. The model was trained to generate text, predict the degree of patient severity, and identify medical findings. Multi-task learning, especially with text generation prioritisation, improved performance over single-task baselines across language generation metrics. The framework also mitigated overfitting in auxiliary tasks compared to single-task models. Qualitative analysis showed logically coherent narratives and accurate identification of findings, though some repetition and disjointed phrasing remained. This work demonstrates the benefits of multi-modal, multi-task learning for image-to-text generation applications.",
}

Image-to-text generation involves automatically generating descriptive text from images and has applications in medical report generation. However, traditional approaches often exhibit a semantic gap between visual and textual information. In this paper, we propose a multi-task learning framework to leverage both visual and non-imaging data for generating radiology reports. Along with chest X-ray images, 10 additional features comprising numeric, binary, categorical, and text data were incorporated to create a unified representation. The model was trained to generate text, predict the degree of patient severity, and identify medical findings. Multi-task learning, especially with text generation prioritisation, improved performance over single-task baselines across language generation metrics. The framework also mitigated overfitting in auxiliary tasks compared to single-task models. Qualitative analysis showed logically coherent narratives and accurate identification of findings, though some repetition and disjointed phrasing remained. This work demonstrates the benefits of multi-modal, multi-task learning for image-to-text generation applications.
Aksoy, Nurbanu Ravikumar, Nishant Sharoff, Serge
Enhancing Image-to-Text Generation in Radiology Reports through Cross-modal Multi-Task Learning