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Researchers developed the Brazilian labeled chest x-ray dataset (BRAX) to aid machine learning (ML) model validation. This large, radiologist-verified dataset features 40,967 images with 14 labels derived from reports.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Diagnosis

Background:

  • Chest radiography interpretation requires specialized expertise.
  • Machine learning (ML) offers potential for automated medical image analysis.
  • Development of large, labeled datasets is crucial for training and validating ML models in radiology.

Purpose of the Study:

  • To introduce the Brazilian labeled chest x-ray dataset (BRAX).
  • To provide a resource for researchers validating ML models for chest radiograph analysis.
  • To facilitate the advancement of automated diagnostic tools in medical imaging.

Main Methods:

  • Compiled a dataset of 24,959 chest radiography studies (40,967 images) from a Brazilian hospital.
  • Images were verified by trained radiologists and de-identified for patient privacy.
  • Utilized Natural Language Processing (NLP) to extract 14 distinct labels from free-text radiology reports in Brazilian Portuguese.

Main Results:

  • Created BRAX, a large-scale, automatically labeled chest x-ray dataset.
  • Dataset includes 40,967 verified chest radiograph images.
  • 14 labels derived from clinical reports are available for ML model training and validation.

Conclusions:

  • The BRAX dataset serves as a valuable resource for the ML research community.
  • Facilitates the development and validation of AI tools for chest x-ray interpretation.
  • Supports advancements in automated medical image analysis and diagnostic support systems.