Medical Imaging & Radiology Dataset
Medical Imaging Data
Tags and Keywords
£1,000,000
About
A large-scale clinical radiology dataset containing diagnostic imaging studies across CT, X-ray, MRI, and other modalities — paired with diagnostic reports. Sourced from clinical archives and curated for medical AI development, diagnostic assistance tools, and multimodal model training.
Data Product Features
- Multi-modality imaging: CT, X-ray, MRI, and additional radiology types
- Paired diagnostic reports in English for supervised learning
- DICOM format files with structured metadata
- Covers diverse anatomical regions and clinical conditions
- De-identified in compliance with applicable privacy regulations
- Volumes and case mix arranged per project scope
Distribution
Format: DICOM + Diagnostic Reports
Size: Multi-million studies
Records: Arranged per project scope
Data Volume
Large-scale clinical imaging corpus spanning multiple study types and anatomical regions. Exact volume arranged per project scope. Sample available for evaluation before purchase.
Usage
- Training diagnostic AI and radiology assistance models
- Medical image segmentation and classification
- Anomaly and pathology detection in clinical scans
- Building foundational models for healthcare AI
- Computer-aided diagnosis (CAD) research
- Multimodal AI combining imaging and clinical text
Coverage
Geographic Coverage: India-origin clinical sources
Time Range: Clinical archives
Imaging Modalities: CT, X-ray, MRI, and additional types
Language of Reports: English
License
CC0 — No Rights Reserved
AI Training Rights
Licensee is granted a non-exclusive, worldwide, and perpetual right to use this data product to train, fine-tune, and evaluate machine learning models. The data product itself may not be redistributed or shared outside licensed usage. Licensee must comply with all applicable laws, including data protection and privacy regulations.
Who Can Use It
- Radiology AI Companies: For training diagnostic and triage models
- Healthcare AI Researchers: For medical image segmentation and anomaly detection
- Hospital Tech Teams: For building AI-assisted diagnostic tools
- Academic Institutions: For medical imaging benchmarking and research
Data Dictionary
- study_id (string) — Unique identifier for each radiology study
- modality (string) — Imaging type: CT, X-ray, MRI
- body_region (string) — Anatomical region examined
- acquisition_date (date) — Date of study in YYYY-MM-DD format
- patient_age_group (string) — De-identified age bracket
- patient_gender (string) — De-identified gender
- diagnostic_report (text) — Radiologist findings and impression
- file_format (string) — DICOM or derived format
- institution_code (string) — Anonymised source institution
- de_identification_status (string) — Confirmed de-identified flag
- number_of_images (integer) — Image count per study
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£1,000,000
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