Commercial

CT Scan Chest Dataset

It is a large-scale CT scan chest dataset featuring over 150,000 chest CT images with annotated pathologies, designed for training deep learning models in lung disease detection, cancer diagnosis, and medical imaging tasks, with labeled data covering a wide range of conditions such as pulmonary embolism, tuberculosis, and lung cancer.

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  • studies with protocol
    50,000+
  • studies without protocol
    100,000+
  • pathologies
    24
  • Medicine
  • Computer vision
  • Machine Learning
  • Segmentation
  • Classification
  • studies with protocol
    50,000+
  • studies without protocol
    100,000+
  • pathologies
    24

Dataset Info

Characteristic Data
Description Chest CT scans with or without protocols
Data types DiCOM
Markup Segmentation of pathologies
Tasks Pathology recognition, computer vision.
Number of studies 150,000+
Labeling Information about each study, including target pathology (1 for presence, 0 for absence)
Pathologies Developmental anomalies of the lower airways, Destruction/abscess of the lung, Chest soft tissue changes (breast or mammary gland masses), Pulmonary embolism, Pulmonary airflow disorders, Lung cancer, Osteoporosis, Osteoporosis, Hydrothorax, Coronary calcium, Aortic aneurysm, Pulmonary trunk diameter, Lymph nodes, Pulmonary emphysema, Tuberculosis, Sarcoidosis.
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Technical
Characteristics

Characteristic Data
File extension DiCOM
Extension of labeling file csv
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Healthcare and Diagnostics

    Enhancing lung disease detection

    CT Scan Chest Dataset helps hospitals and clinics train diagnostic systems for lung diseases, including COVID-19 cases and lung cancer. The dataset contains annotated CT images and radiology reports, making it suitable for improving cancer detection and supporting faster medical decisions in clinical practice.

  • Medical Research

    Training deep learning models in imaging

    This is valuable for academic research in medical imaging and deep networks. Researchers can use it as training data for image classification, lung segmentations, and machine learning pipelines. It supports innovation in tomography scans and advanced diagnostic algorithms.

  • Radiology and Imaging Centers

    Improving CT examinations

    CT Scan Chest Dataset offers a reliable collection of CT scans and X-ray images for radiology centers. By using this dataset, experienced radiologists and AI teams can refine image quality, test detection algorithms, and validate diagnostic models against real chest computed cases.

  • Public Health and AI Development

    Detecting and monitoring COVID-19

    This CT chest labeled dataset plays a key role in diagnosing COVID-19. With publicly available medical images, it supports the development of learning models that help identify infection patterns, track disease progression, and support early intervention strategies in large-scale healthcare systems.

FAQs

What are the technical characteristics of CT Scan Chest Dataset?
The dataset is stored in DiCOM files with annotations in CSV format. It includes detailed information for each study, covering 3D chest imaging, radiology reports, and pathology segmentation for advanced machine learning research.
Does the dataset include radiology reports or text metadata?
Yes, each study includes structured radiology text and metadata annotations. These details provide insights into chest computed tomography scans, patient demographics, and radiology findings, which are crucial for diagnostic imaging models.
Which medical conditions are covered in this CT Scan Chest Dataset?
The dataset covers a broad spectrum of lung diseases and related conditions, including pulmonary airflow disorders, osteoporosis, hydrothorax, aortic aneurysm, sarcoidosis, and pulmonary embolism. This variety makes it useful for training data in multiple medical imaging applications.
Can I request a sample of the dataset before purchase?
Yes, Unidata provides samples upon request. This allows you to verify CT images, DiCOM format compatibility, and annotation quality before using them in deep learning or machine learning workflows.
Still have questions about using Unidata datasets? Read our user-guides

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