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Knowledge matters: Chest radiology report generation with general and specific knowledge.

Shuxin Yang1, Xian Wu2, Shen Ge2

  • 1Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology, CAS, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China.

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Summary

This study introduces a knowledge-enhanced approach for automatic chest radiology report generation, improving accuracy by integrating general and specific medical knowledge. The method significantly outperforms existing techniques on the IU-Xray dataset.

Keywords:
Chest radiology report generationKnowledge graphMulti-head attention

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Automatic chest radiology report generation aids radiologists by reducing workload and preventing diagnostic errors.
  • Current encoder-decoder models, while common, face challenges with visual-textual bias and a lack of expert medical knowledge.

Purpose of the Study:

  • To develop a novel knowledge-enhanced approach for automatic chest radiology report generation.
  • To address limitations of pure data-driven methods by incorporating medical expertise.
  • To improve the accuracy and reliability of generated radiology reports.

Main Methods:

  • Proposed a knowledge-enhanced framework integrating general (input-independent) and specific (input-dependent) medical knowledge.
  • Developed a knowledge-enhanced multi-head attention mechanism to merge visual features with both knowledge types.
  • Evaluated the model on the IU-Xray and MIMIC-CXR datasets.

Main Results:

  • The knowledge-enhanced approach significantly outperformed state-of-the-art methods on the IU-Xray dataset across most metrics.
  • Performance on the MIMIC-CXR dataset was comparable to existing state-of-the-art methods.
  • Ablation studies confirmed the positive impact of both general and specific knowledge on report generation quality.

Conclusions:

  • Integrating general and specific medical knowledge enhances automatic chest radiology report generation.
  • The proposed knowledge-enhanced multi-head attention mechanism effectively utilizes diverse knowledge sources.
  • This approach offers a promising direction for improving clinical diagnostic support systems.