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Prior Knowledge Enhances Radiology Report Generation.

Song Wang1, Liyan Tang1, Mingquan Lin2

  • 1The University of Texas at Austin, Austin, TX, USA.

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This study introduces a knowledge graph to improve AI-generated radiology reports by considering medical finding relationships. The new method enhances report accuracy and quality, outperforming previous approaches.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Informatics
  • Natural Language Processing

Background:

  • Radiology report generation using AI aims to reduce radiologist workload.
  • Current deep learning models often overlook interdependencies between medical findings, limiting report quality.

Purpose of the Study:

  • To develop a novel method for radiology report generation by incorporating knowledge graphs.
  • To improve the accuracy and quality of AI-generated radiology reports by representing associations among medical findings.

Main Methods:

  • Constructed an informative knowledge graph to represent associations among medical findings.
  • Integrated this prior knowledge into a deep learning model for radiology report generation.
  • Evaluated the model on the IU X-ray dataset.

Main Results:

  • The proposed method achieved superior performance on the IU X-ray dataset.
  • Achieved ROUGE-L of 0.384±0.007 and CIDEr of 0.340±0.011.
  • Demonstrated an average improvement of 1.6% over previous methods (2.0% in CIDEr, 1.5% in ROUGE-L).

Conclusions:

  • Incorporating prior knowledge through knowledge graphs significantly enhances radiology report generation.
  • The findings suggest that representing medical finding associations is crucial for improving AI diagnostic accuracy.
  • The developed model offers a promising advancement in computer-aided diagnosis for radiology.