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XRaySwinGen: Automatic medical reporting for X-ray exams with multimodal model.

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This study introduces an AI model for generating detailed radiology reports from X-rays, improving diagnostic accuracy. The multimodal approach enhances medical image captioning for better patient condition insights.

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Computer Vision and Natural Language Processing

Background:

  • Manual radiology report generation is time-consuming and prone to errors.
  • Current AI in medical imaging often focuses on classification, missing crucial patient condition details.
  • Generating descriptive reports from X-rays remains a challenge for AI.

Purpose of the Study:

  • To develop a multimodal AI model for generating detailed, descriptive radiology reports from X-ray images.
  • To overcome limitations in current AI medical image captioning by focusing on comprehensive report generation.
  • To improve the accuracy and efficiency of medical reporting through advanced AI techniques.

Main Methods:

  • Utilized a multimodal model combining Computer Vision (Swin Transformer) for image encoding and Natural Language Processing (GPT-2) for text decoding.
  • Implemented Swin Transformer for hierarchical image representation, enhancing perception without increasing computational cost.
  • Employed cross-attention layers and bilingual training (Portuguese PT-BR and English) for robust report generation.

Main Results:

  • Achieved promising performance on a proposed database with ROUGE-L of 0.748 and METEOR of 0.741.
  • Demonstrated effectiveness on the NIH CHEST X-ray dataset, yielding ROUGE-L of 0.404 and METEOR of 0.393.
  • The model successfully generated descriptive textual reports from X-ray images.

Conclusions:

  • The proposed multimodal AI model effectively generates detailed radiology reports, addressing limitations in current medical image captioning.
  • The innovative use of Swin Transformer and GPT-2 offers an efficient and effective approach to AI-driven medical report generation.
  • Bilingual training and promising results indicate the model's potential for improving radiology workflows and diagnostic capabilities.