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Soft label-guided transformer for radiology report generation.

Xinyao Liu1, Junchang Xin2, Qi Shen1

  • 1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.

Journal of Biomedical Informatics
|October 15, 2025
PubMed
Summary

This study introduces a soft label-guided transformer (SLGT) to improve automatic radiology report generation. The SLGT model better simulates a radiologist's process, leading to more accurate descriptions of medical images and aiding computer-aided diagnosis.

Keywords:
Attention mechanismAutomatic generation of radiology reportChest X-rayEncoder–decoder frameworkTransformer

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Radiology reports are crucial for physician decision-making.
  • Automatic report generation can enhance efficiency but often lacks detail.
  • Current methods struggle to accurately describe specific lesion features.

Purpose of the Study:

  • To develop an automated radiology report generation method that mimics the radiologist's diagnostic process.
  • To improve the accuracy and detail of generated radiology reports, especially for abnormal findings.

Main Methods:

  • Proposed a soft label-guided transformer (SLGT) model.
  • Employed a soft label-guided attention mechanism to focus on disease-related features.
  • Aligned image and text features and used generated text to guide representations.
  • Utilized a hybrid loss function for text generation, disease classification, and visual-textual alignment.

Main Results:

  • The SLGT model was evaluated on IU X-ray, MIMIC-CXR, and COV-CTR datasets.
  • SLGT demonstrated superior performance compared to existing state-of-the-art models across all tested datasets.

Conclusions:

  • The developed SLGT model significantly enhances automatic radiology report generation.
  • This advancement makes automated reports more feasible for computer-aided diagnosis applications.