@inproceedings{bu-etal-2024-dynamic,
    title = "Dynamic Knowledge Prompt for Chest {X}-ray Report Generation",
    author = "Bu, Shenshen  and
      Song, Yujie  and
      Li, Taiji  and
      Dai, Zhiming",
    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.482",
    pages = "5425--5436",
    abstract = "Automatic generation of radiology reports can relieve the burden of radiologist. In the radiology library, the biased dataset and the sparse features of chest X-ray image make it difficult to generate reports. Many approaches strive to integrate prior information to enhance generation, but they fail to dynamically utilize pulmonary lesion knowledge at the instance-level. To alleviate above problem, we propose a novel Dynamic Knowledge Prompt (DKP) framework for chest X-ray report generation. The DKP can dynamically incorporate the pulmonary lesion information at the instance-level to facilitate report generation. Initially, we design a knowledge prompt for each pulmonary lesion using numerous radiology reports. After that, the DKP using an anomaly detector generates the dynamic knowledge prompt by extracting discriminative lesion features in the corresponding X-ray image. Finally, the knowledge prompt is encoded and fused with hidden states extracted from decoder, to form multi-modal features that guide visual features to generate reports. Extensive experiments on the public datasets MIMIC-CXR and IU X-Ray show that our approach achieves state-of-the-art performance.",
}

Automatic generation of radiology reports can relieve the burden of radiologist. In the radiology library, the biased dataset and the sparse features of chest X-ray image make it difficult to generate reports. Many approaches strive to integrate prior information to enhance generation, but they fail to dynamically utilize pulmonary lesion knowledge at the instance-level. To alleviate above problem, we propose a novel Dynamic Knowledge Prompt (DKP) framework for chest X-ray report generation. The DKP can dynamically incorporate the pulmonary lesion information at the instance-level to facilitate report generation. Initially, we design a knowledge prompt for each pulmonary lesion using numerous radiology reports. After that, the DKP using an anomaly detector generates the dynamic knowledge prompt by extracting discriminative lesion features in the corresponding X-ray image. Finally, the knowledge prompt is encoded and fused with hidden states extracted from decoder, to form multi-modal features that guide visual features to generate reports. Extensive experiments on the public datasets MIMIC-CXR and IU X-Ray show that our approach achieves state-of-the-art performance.
Bu, Shenshen Song, Yujie Li, Taiji Dai, Zhiming
Dynamic Knowledge Prompt for Chest X-ray Report Generation