Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation
<|reference_start|>Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation: A radiology report comprises presentation-style vocabulary, which ensures clarity and organization, and factual vocabulary, which provides accurate and objective descriptions based on observable findings. While manually writing these reports is time-consuming and labor-intensive, automatic report generation offers a promising alternative. A critical step in this process is to align radiographs with their corresponding reports. However, existing methods often rely on complete reports for alignment, overlooking the impact of presentation-style vocabulary. To address this issue, we propose FSE, a two-stage Factual Serialization Enhancement method. In Stage 1, we introduce factuality-guided contrastive learning for visual representation by maximizing the semantic correspondence between radiographs and corresponding factual descriptions. In Stage 2, we present evidence-driven report generation that enhances diagnostic accuracy by integrating insights from similar historical cases structured as factual serialization. Experiments on MIMIC-CXR and IU X-ray datasets across specific and general scenarios demonstrate that FSE outperforms state-of-the-art approaches in both natural language generation and clinical efficacy metrics. Ablation studies further emphasize the positive effects of factual serialization in Stage 1 and Stage 2. The code is available at https://github.com/mk-runner/FSE.<|reference_end|>
@article{liu2024factual,
    title={Factual Serialization Enhancement: A Key Innovation for Chest X-ray
  Report Generation},
    author={Kang Liu and Zhuoqi Ma and Mengmeng Liu and Zhicheng Jiao and Xiaolu
  Kang and Qiguang Miao and Kun Xie},
    journal={arXiv preprint arXiv:2405.09586},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09586},
    primaryClass={eess.IV cs.AI cs.CV}
}