19M+ MRI Scan Images Without Findings Dataset
Medical Imaging Data
Tags and Keywords
£37,000
About
19M+ MRI Scan Images Without Findings Dataset
Description
The 19M+ MRI Scan Images Without Findings Dataset is a high-quality medical imaging dataset designed for artificial intelligence, computer vision, and radiology research. It contains over 19M+ Magnetic Resonance Imaging (MRI) scan images collected from 63K+ anonymized patients in DICOM format, making it suitable for developing, training, and evaluating medical imaging algorithms. The dataset supports a wide range of healthcare AI applications, including image classification, segmentation, anomaly detection, and diagnostic assistance. Its standardized DICOM format ensures seamless integration with clinical imaging systems, research workflows, and deep learning pipelines.
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 |
|---|---|
| MRI Image | Magnetic Resonance Imaging (MRI) scan stored in DICOM format. |
| Modality | Imaging modality (MRI). |
| Body Region | Anatomical region scanned. |
| Image Dimensions | Width and height of the MRI image. |
| Pixel Depth | Bit depth of the DICOM image. |
Distribution
- Dataset Format: DICOM (.dcm)
Data Volume
- 19M+ MRI Scan Images
- Single imaging modality (MRI)
- High-resolution medical imaging files
- Standardized DICOM format
- Dataset Size: The dataset size may vary depending on image resolution, image dimensions, compression quality, metadata availability, annotations, and dataset version.
Usage
This dataset is ideal for a variety of AI and medical imaging applications:
- Medical Image Classification: Train deep learning models to classify MRI images.
- Medical Image Segmentation: Develop AI models for organ, tissue, and lesion segmentation.
- Anomaly Detection: Detect abnormalities and rare imaging patterns.
- Computer Vision Research: Build and evaluate medical image analysis algorithms.
- Radiology AI: Develop automated imaging workflows and diagnostic support systems.
- Medical Imaging Education: Support teaching and training in radiology and medical AI.
- Foundation Model Training: Train vision models for healthcare and medical imaging.
- Academic Research: Conduct studies in medical image processing 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 | MRI |
| Body_Region | String | Anatomical region scanned | Brain, Spine, Knee, Abdomen, etc. (if available) |
| Sequence_Type | String | MRI acquisition sequence | T1, T2, FLAIR, DWI, ADC, STIR, etc. (if available) |
| Image_File | String | DICOM image file | .dcm |
| Image_Width | Integer | Image width in pixels | Positive integer |
| Image_Height | Integer | Image height in pixels | Positive integer |
| Pixel_Depth | Integer | Image bit depth | 8-bit, 12-bit, 16-bit, etc. |
Additional Notes
- This dataset contains MRI scan images only and does not include radiology findings, annotations, or diagnostic reports.
- Images are stored in the industry-standard DICOM format, ensuring compatibility with PACS systems, DICOM viewers, and medical AI frameworks.
- Suitable for computer vision, medical image preprocessing, self-supervised learning, transfer learning, segmentation, classification, anomaly detection, and healthcare AI research.
Considerations
This dataset is provided for research and educational purposes only. It contains only sample data.
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£37,000
Download Dataset in IMAGE Format
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