Medical Imaging: MRI Dataset
Enhance your AI model with over 42,000 high-quality MRI scans in original Digital Imaging and Communications in Medicine (DICOM) format. Perfect for computer vision healthcare applications like medical image segmentation and disease recognition and diagnosis.
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Use this AI dataset to:
Enhance Clinical Decision Support: Use this wide array of diagnostic images and video with detailed annotations for real-time identification and diagnosis, enriching AI models with the capability to support clinicians to make informed decisions.
Advance Medical Research: Leverage the diverse collection of images and video in their original DICOM format to uncover patterns and support research in medical imaging, contributing to early disease detection and improved health outcomes.
AI use cases:
Medical Image Segmentation: Train AI models to segment different anatomical structures and tissues within MRI scans. This dataset can help precisely locate abnormalities such as brain tumors for surgical planning or disease diagnosis.
Disease Detection and Diagnosis: Train AI algorithms to analyze MRI scans and detect signs of diseases or medical conditions, including neurological disorders, musculoskeletal injuries, cardiovascular diseases and oncological abnormalities. AI can also assist radiologists to identify subtle changes indicative of pathology.
Functional MRI (fMRI) Analysis: Apply AI methods to analyze fMRI data to study brain activity, connectivity patterns and cognitive functions. AI-based fMRI analysis can contribute to neuroscience research, psychiatric diagnosis and understanding neurological disorders.
Technical Specifications
- Type: Image & Video
- Quantity: Over 42,000
- Data Type: Medical Images
- Domain: Healthcare
- File Format: DCM (DICOM)
- Demographic Description: pt-BZ
- Metadata/Annotation: Available as an added service
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License Information
This dataset is covered by our standard Data license agreement. The license agreement is perpetual and allows for the commercialization of all models built on the data.
