Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation
<|reference_start|>Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation: The automated generation of imaging reports proves invaluable in alleviating the workload of radiologists. A clinically applicable reports generation algorithm should demonstrate its effectiveness in producing reports that accurately describe radiology findings and attend to patient-specific indications. In this paper, we introduce a novel method, \textbf{S}tructural \textbf{E}ntities extraction and patient indications \textbf{I}ncorporation (SEI) for chest X-ray report generation. Specifically, we employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports and improve the quality of factual entity sequences. This reduces the noise in the following cross-modal alignment module by aligning X-ray images with factual entity sequences in reports, thereby enhancing the precision of cross-modal alignment and further aiding the model in gradient-free retrieval of similar historical cases. Subsequently, we propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications. This process allows the text decoder to attend to discriminative features of X-ray images, assimilate historical diagnostic information from similar cases, and understand the examination intention of patients. This, in turn, assists in triggering the text decoder to produce high-quality reports. Experiments conducted on MIMIC-CXR validate the superiority of SEI over state-of-the-art approaches on both natural language generation and clinical efficacy metrics.<|reference_end|>
@article{liu2024structural,
    title={Structural Entities Extraction and Patient Indications Incorporation for
  Chest X-ray Report Generation},
    author={Kang Liu and Zhuoqi Ma and Xiaolu Kang and Zhusi Zhong and Zhicheng
  Jiao and Grayson Baird and Harrison Bai and Qiguang Miao},
    journal={arXiv preprint arXiv:2405.14905},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14905},
    primaryClass={eess.IV cs.AI cs.CL}
}