178K+ X-Ray Images with Radiology Findings Dataset
Medical Imaging Data
Tags and Keywords
£664,600
About
178K+ X-Ray Images with Radiology Findings Dataset
Description
The 178K+ X-Ray Images with Radiology Findings Dataset is a large-scale medical imaging dataset designed for artificial intelligence, computer vision, radiology research, and healthcare analytics. The dataset contains over 178K+ digital X-ray images in DICOM format paired with corresponding radiology findings collected from 112K+ anonymized patients in PDF reports. It is developed to support AI-powered disease detection, medical image analysis, radiology report generation, multimodal learning, and clinical decision support systems. Covering multiple anatomical regions and radiological observations, this dataset provides high-quality imaging and reporting data for training, validating, and deploying next-generation healthcare AI models.
Note: Pricing varies depending on several factors, including the number of image-report pairs, 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. |
| Radiology Findings | Radiologist interpretation associated with each X-ray examination. |
| Modality | Imaging modality (X-Ray). |
| Body Region | Anatomical region imaged (Chest, Abdomen, Spine, Hand, Foot, Pelvis, etc.). |
| View Position | X-ray projection or view (AP, PA, Lateral, Oblique, etc.). |
| Findings Summary | Summary of radiological observations. |
| Report File | Corresponding radiology report in PDF format. |
| Image Dimensions | Resolution and image size information. |
Distribution
-
Dataset Format: DICOM (.dcm), PDF (.pdf)
-
Dataset Structure:
- Organized by patient or study folders
- X-ray images stored in DICOM format
- Corresponding radiology findings provided as PDF reports
- Standardized image-to-report mapping for multimodal AI applications
Data Volume
- 178K+ X-Ray Images
- 112K+ anonymized patients
- Multiple imaging studies across various anatomical regions
- High-resolution DICOM medical imaging files
- Dataset Size: The dataset size may vary depending on image resolution, image dimensions, compression quality, metadata availability, diagnostic findings, annotations, and dataset version.
Usage
This data product is ideal for a variety of healthcare AI applications:
- Medical Image Classification: Train AI models for X-ray image classification.
- Disease Detection: Develop algorithms to detect fractures, pneumonia, tuberculosis, lung nodules, arthritis, and other abnormalities.
- Medical Image Segmentation: Build models for organ, bone, and lesion segmentation.
- Radiology AI: Develop automated radiology interpretation and reporting systems.
- Computer Vision: Train deep learning models for medical image understanding.
- Vision-Language Models: Train multimodal AI models using X-ray images and radiology findings.
- Medical Report Analysis: Extract structured clinical information from radiology reports.
- Clinical Decision Support: Develop AI systems that assist healthcare professionals in diagnosis.
- 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 projection | AP, PA, Lateral, Oblique, etc. |
| Image_File | String | X-ray image file | .dcm |
| Report_File | String | Radiology findings report | |
| Findings | Text | Radiologist interpretation | Free-text clinical observations |
| Diagnosis | String | Clinical diagnosis | Disease or condition |
| 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
- The dataset combines X-ray images with corresponding radiology findings, making it suitable for computer vision, multimodal AI, vision-language models, and medical report generation.
- Images are stored in the industry-standard DICOM format, while radiology findings are provided in PDF format.
- Suitable for disease detection, image classification, segmentation, report generation, multimodal learning, clinical decision support, and radiology AI research.
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£664,600
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