27M+ MRI Scan Images With Findings Dataset
Medical Imaging Data
Tags and Keywords
£37,000
About
27M+ MRI Scan Images With Findings Dataset
Description
The 27M+ MRI Scan Images With Findings Dataset is a comprehensive medical imaging dataset designed for artificial intelligence, computer vision, radiology research, and healthcare analytics. It contains over 27M+ MRI scan images collected from 62K+ anonymized patients in DICOM format accompanied by corresponding radiology findings in PDF reports. The dataset enables the development of AI-powered diagnostic systems, medical image analysis models, disease detection algorithms, and clinical decision support solutions. Covering a wide range of anatomical regions and imaging findings, this dataset provides high-quality medical imaging data suitable for research, model training, and healthcare innovation.
Note: The listed price applies to the specified initial batch of 5,000 image-report pairs. Pricing for larger batches or the complete dataset library varies based on the number of image-report pairs, 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 |
|---|---|
| MRI Image | Magnetic Resonance Imaging (MRI) scan stored in DICOM format. |
| Findings Report | Radiologist findings associated with each MRI study in PDF format. |
| Modality | Imaging modality (MRI). |
| Report File | Linked PDF report corresponding to the MRI study.. |
| Body Region | Anatomical region scanned (Brain, Spine, Knee, Abdomen, etc.). |
| Findings Summary | Clinical interpretation extracted from the radiology report. |
Distribution
- Dataset Format: DICOM (.dcm), PDF (.pdf)
Data Volume
- 27M+ MRI Scan Images
- collected from 62K+ anonymized patients
- Multiple MRI studies and anatomical regions
- High-resolution medical imaging data
- 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 dataset is ideal for a variety of healthcare AI and medical imaging applications:
- Medical Image Classification: Train AI models for MRI image classification.
- Disease Detection: Develop algorithms for identifying tumors, lesions, fractures, neurological disorders, and other abnormalities.
- Radiology AI: Build automated radiology interpretation systems.
- Computer Vision: Train deep learning models for medical image understanding.
- Clinical Decision Support: Assist healthcare professionals in diagnosis.
- Medical Report Analysis: Link MRI images with radiology findings for multimodal AI.
- Medical NLP: Extract structured information from radiology reports.
- Foundation Model Training: Train multimodal healthcare AI and vision-language models.
- Academic Research: Medical imaging, radiology, and healthcare analytics research.
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 | MRI |
| Body_Region | String | Anatomical region scanned | Brain, Spine, Knee, Abdomen, Chest, Pelvis, Shoulder, etc. |
| Sequence_Type | String | MRI acquisition sequence | T1, T2, FLAIR, DWI, ADC, SWI, STIR, etc. |
| Image_File | String | DICOM image file path | .dcm |
| Report_File | String | Radiology findings document | |
| Findings | Text | Radiologist interpretation | Free-text clinical findings |
| Image_Width | Integer | Image width in pixels | Positive integer |
| Image_Height | Integer | Image height in pixels | Positive integer |
Additional Notes
-
Designed for medical imaging AI, computer vision, radiology, multimodal learning, and healthcare machine learning applications.
-
DICOM images are paired with corresponding PDF radiology findings, enabling image-to-report and vision-language model development.
-
Suitable for supervised learning, foundation model training, anomaly detection, disease classification, medical image segmentation, report generation, and diagnostic assistance.
Considerations
This dataset is provided for research and educational purposes only. It contains only sample data.
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£37,000
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