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Chest X-Ray Dataset

The NIH chest X-ray dataset contains labeled chest radiographs with detailed segmentation of pathologies, providing high-quality X-ray images of chest for detecting lung diseases, pulmonary conditions, and other medical imaging tasks, making it a valuable resource for diagnostic imaging, computer vision, and deep learning in healthcare

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  • Files
    443
  • Medical Studies
    150
  • Data tags
    13
  • Medicine
  • Computer Vision
  • Segmentation
  • Classification
  • Machine Learning
  • Files
    443
  • Medical Studies
    150
  • Data tags
    13

Dataset Info

Characteristic Data
Description Chest X-ray to recognize pathologies
Data types DiCOM
Markup Segmentation of pathologies
Tasks Pathology recognition, computer vision.
Total number of files 443
Number of studies 150
Labeling ‘Nodule/mass’, ‘Dissemination’, ‘Annular shadows’, ‘Petrifications’, ‘Pleural effusion’, ‘Pneumothorax’, ‘Rib fractures’, ‘Healed rib fracture’, ‘Atelectasis’, ‘Enlarged mediastinum’, ‘Hilar enlargement’, ‘Infiltration/Consolidation’, ‘Fibrosis’
Gender Male, female
Age 25 - 70
chest_xray
chest_xray_labeled
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Statistics

Distribution by gender
Number of studies for each condition

Technical
Characteristics

Characteristic Data
File extension DiCOM
Markup format JSON
Source and collection methodology: Data was collected by a partner of Unidata

Dataset Use Cases

  • Medical Research & Radiology

    Advancing Studies in Lung Diseases and Chest Conditions

    Chest X-Ray Dataset provides high-quality chest radiographs with labels for lung conditions such as pneumonia, pleural effusions, and lung cancer. Researchers use this medical imaging study to analyze chest X-rays, improve diagnostic accuracy, and explore early detection of pulmonary diseases using lateral views and PA view data.



  • AI & Machine Learning

    Training Models for Chest X-Ray Analysis

    This labeled dataset serves as robust training data for machine learning and deep learning models. With X-ray chest images and detailed annotations, it enables developers to train algorithms that detect lung tissue abnormalities, classify chest walls, and support diagnostic imaging tasks in clinical applications.



  • Healthcare & Diagnostics

    Improving Clinical Decision Support Systems

    Hospitals and diagnostic centers apply such datasets to strengthen recognition systems that assist radiologists in interpreting chest X-rays. By analyzing lung x-ray photos showing pleural fluid, right heart enlargement, or left diaphragm abnormalities, clinicians can confirm findings faster and reduce errors in chest radiography.



  • Medical Education & Training

    Supporting Radiology Learning and Case Studies

    The X-ray datasets are widely used in medical education for training radiology students. Labeled chest radiographs highlighting common and rare medical conditions allow learners to study lung diseases, recognize signs of chest pain, and compare findings with CT scans for a deeper understanding of diagnostic imaging.



FAQs

What file formats are included?
The chest X-ray images are provided in DiCOM format, while annotations are available in JSON format. This ensures compatibility with medical imaging software and AI diagnostic pipelines.
How was Chest X-Ray Dataset collected?
Data was collected in collaboration with a Unidata partner from real diagnostic imaging studies. Images were processed, anonymized, and annotated by medical experts to provide high-quality, clinically accurate training data.
What types of annotations are provided?
Annotations include pathology labels such as “Nodule/mass,” “Pleural effusion,” “Pneumothorax,” “Fibrosis,” and “Atelectasis.” Each chest radiograph is manually labeled to ensure precision for training diagnostic algorithms.
Can I request a sample of the dataset before purchasing?
Yes, Unidata provides sample chest X-rays with labels to verify image quality, manual annotations, and segmentation accuracy. This allows testing for deep learning models, computer vision tasks, and chest radiograph analysis before purchase.
Still have questions about using Unidata datasets? Read our user-guides

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