187K+ X-Ray Images Dataset
Medical Imaging Data
Tags and Keywords
£278,400
About
187K+ X-Ray Images Dataset
Description
The 187K+ X-Ray Images Dataset is a large-scale medical imaging dataset designed for artificial intelligence, computer vision, radiology research, and healthcare analytics. The dataset contains over 187K+ X-ray images collected from 79K+ anonymized patients in DICOM format, providing high-quality medical imaging data for developing, training, and evaluating AI models. It supports a wide range of healthcare applications, including image classification, disease detection, anomaly detection, segmentation, and clinical imaging research. The standardized DICOM format ensures seamless integration with PACS systems, radiology workflows, and deep learning frameworks.
Note: Pricing varies depending on several factors, including the number of medical images, image quality and resolution, metadata availability, annotation requirements, imaging modality, file formats, and customization needs. The final price will be determined based on the specific dataset requirements.
Data Product Features
| Feature | Description |
|---|---|
| X-Ray Image | Digital radiography image stored in DICOM format. |
| Modality | Imaging modality (X-Ray). |
| Body Region | Anatomical region imaged (Chest, Abdomen, Spine, Hand, Foot, Pelvis, etc.). |
| View Position | X-ray projection/view (e.g., AP, PA, Lateral) . |
| Image Dimensions | Resolution and image size information. |
Distribution
- Dataset Format: DICOM (.dcm)
Data Volume
- 187K+ X-Ray Images
- 79K+ anonymized patients
- Multiple imaging studies across various anatomical regions
- High-resolution DICOM medical imaging files
- Single imaging modality (X-Ray)
- 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 to classify X-ray images.
- Disease Detection: Develop algorithms for identifying abnormalities in radiographic images.
- Medical Image Segmentation: Build models for organ, bone, and tissue segmentation.
- Anomaly Detection: Detect fractures, lesions, and other imaging abnormalities.
- Computer Vision: Train deep learning models for medical image understanding.
- Radiology AI: Develop AI-assisted radiographic analysis systems.
- Clinical Decision Support: Support healthcare professionals with AI-powered imaging tools.
- 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 | X-Ray |
| Body_Region | String | Anatomical region imaged | Chest, Abdomen, Spine, Pelvis, Hand, Foot, etc. |
| View_Position | String | Radiographic view | AP, PA, Lateral, Oblique, etc. |
| Image_File | String | X-ray 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 X-ray images only and does not include radiology findings.
- Images are stored in the industry-standard DICOM format, ensuring compatibility with PACS systems, DICOM viewers, and healthcare AI frameworks.
- Suitable for computer vision, medical image preprocessing, self-supervised learning, transfer learning, image classification, segmentation, anomaly detection, and radiology AI research.
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£278,400
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