12M+ CT Scan Images Dataset
Medical Imaging Data
Tags and Keywords
£37,000
About
12M+ CT Scan Images Dataset
Description
The 12M+ CT Scan Images Dataset is a large-scale medical imaging dataset designed for artificial intelligence, computer vision, radiology research, and healthcare analytics. The dataset contains over 12M+ Computed Tomography (CT) scan images collected from 41K+ anonymized patientsin DICOM format, providing high-quality imaging data for developing, training, and evaluating AI models. It supports a wide range of medical imaging applications, including image classification, segmentation, object detection, anomaly detection, and clinical imaging research. The standardized DICOM format ensures compatibility with healthcare imaging systems, PACS environments, and modern deep learning workflows.
Note: The listed price applies to the specified initial batch of 10,000 images. Pricing for larger batches or the complete dataset library varies based on dataset volume, image quality and resolution, metadata availability, annotation requirements, imaging modality, file formats, licensing terms, and customization needs. Final pricing will be determined based on the specific dataset requirements.
Data Product Features
| Feature | Description |
|---|---|
| CT Scan Image | Computed Tomography (CT) image stored in DICOM format. |
| Modality | Imaging modality (CT). |
| Body Region | Anatomical region scanned (if available). |
| Image Dimensions | Resolution and image size information. |
Distribution
- Dataset Format: DICOM (.dcm)
Data Volume
- 12M+ CT Scan Images
- 41K+ anonymized patients
- Multiple CT studies across various anatomical regions
- High-resolution DICOM medical imaging files
- Single imaging modality (CT)
- Dataset Size: The dataset size may vary depending on image resolution, image dimensions, compression quality, metadata availability, annotations, and dataset version.
Usage
This data product is ideal for a variety of applications:
- Medical Image Classification: Train AI models for CT image classification.
- Medical Image Segmentation: Develop algorithms for organ, tissue, and lesion segmentation.
- Anomaly Detection: Detect abnormalities and unusual imaging patterns.
- Computer Vision: Build deep learning models for medical image understanding.
- Radiology AI: Develop AI-assisted imaging analysis workflows.
- Clinical Decision Support: Support AI-assisted healthcare applications.
- Foundation Model Training: Train vision models for medical imaging.
- Academic Research: Advance research in radiology, medical imaging, and healthcare AI.
Coverage
- Geographic Coverage: Global
License
CC BY 4.0 (Creative Commons Attribution 4.0 International)
AI Training Rights
InfoBay.AI ensures that all datasets are sourced, curated, and managed with proper ownership verification, licensing documentation, and data provenance records. We hold the necessary rights to license and sublicense the datasets we provide through formal agreements with our data vendors, which grant us the required permissions for commercial licensing and AI training use cases. To ensure transparency and compliance, we maintain relevant documentation and have previously shared redacted agreements for selected datasets as evidence of our data rights and licensing authority.
Data Dictionary
| Column Name | Data Type | Description | Possible Values/Notes |
|---|---|---|---|
| Modality | String | Imaging modality | CT |
| Body_Region | String | Anatomical region scanned | Brain, Chest, Abdomen, Pelvis, Spine, etc. |
| Image_File | String | CT image file | .dcm |
| Image_Width | Integer | Image width in pixels | Positive integer |
| Image_Height | Integer | Image height in pixels | Positive integer |
Considerations
This dataset is provided for research and educational purposes only. It contains only sample data.
Additional Notes
- This dataset contains CT scan images only and does not include radiology findings, annotations, diagnostic reports, or labels.
- Images are stored in the industry-standard DICOM format, ensuring compatibility with PACS systems, DICOM viewers, and healthcare AI frameworks.
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£37,000
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