DICOM Medical Imaging Dataset for Advanced AI/ML Applications in Healthcare
De-identified DICOM image datasets with preserved metadata—and optional radiology study reports—to accelerate model training, validation, and clinical research.
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DICOM imaging data built for real-world AI
Shaip offers AI-ready DICOM medical imaging datasets designed to help healthcare AI teams build, train, and validate robust models for diagnosis, triage, and decision support—using de-identified data that preserves clinical value.
Dataset snapshot
- Total studies:10M+
- Top geographies (by studies): USA, Brazil, and India
- Modalities represented: CR, CT, US, DX, MR, MG, OT, RF, NM, Mammography
- Body parts represented: Chest, Abdomen, Head, Spine, Neck, Heart, & more
Common Use Cases for DICOM Image Datasets
Train diagnostic imaging AI models
- Abnormality detection
- Disease classification
- Severity scoring/staging
- Triage prioritization
- Supports multi-modality development
Validate & benchmark model performance
- Evaluate model accuracy on broader populations
- Benchmark performance by modality/body region
- Run external validation to reduce overfitting
Improve model robustness across devices & sites
- Test generalization across scanners/vendors
- Reduce performance drops when deploying to new hospitals
Build multimodal AI (image + radiology report)
- Derive weak labels from report language
- Train models aligned with report narratives
- Build report-aware triage & decision-support
Clinical research and cohort creation
- Filter cohorts by modality/body part/time
- Support retrospective studies
- Accelerate hypothesis testing while maintaining privacy controls
Annotation & ground-truth creation for ML training
- Classification tags
- Bounding boxes
- Segmentation masks
What you receive in the DICOM Image Dataset
1. DICOM pixel data (the images)
All imagery is de-identified at the pixel level:
- Text on imagery is redacted or pseudonymized
- “De-facing” artifacts may be introduced when facial reconstruction is possible (e.g., high-resolution CT).
3. Study report (optional, when available)
Unstructured narrative text written by the radiologist/doctor, with Safe Harbor anonymization and the same date-shift approach applied.
2. DICOM metadata (with Safe Harbor)
All standard DICOM metadata is preserved for delivery while HIPAA Safe Harbor identifiers are anonymized, including:
- Patient name replaced with Patient ID
- Patient ID cryptographically hashed
- Institution name replaced with an alternative name
- Dates shifted within 365 days (patient-level consistent shift).
4. Custom metadata (optional value-add)
Optional derived metadata can include:
- Parsed Patient Age
- SNOMED tags (from report)
- Positive entities (from report)
- Country of residence (from address)
- Imputed Race / Imputed Ethnicity (derived fields)
1. DICOM pixel data (the images)
All imagery is de-identified at the pixel level:
- Text on imagery is redacted or pseudonymized
- “De-facing” artifacts may be introduced when facial reconstruction is possible (e.g., high-resolution CT).
2. DICOM metadata (with Safe Harbor)
All standard DICOM metadata is preserved for delivery while HIPAA Safe Harbor identifiers are anonymized, including:
- Patient name replaced with Patient ID
- Patient ID cryptographically hashed
- Institution name replaced with an alternative name
- Dates shifted within 365 days (patient-level consistent shift).
3. Study report (optional, when available)
Unstructured narrative text written by the radiologist/doctor, with Safe Harbor anonymization and the same date-shift approach applied.
4. Custom metadata (optional value-add)
Optional derived metadata can include:
- Parsed Patient Age
- SNOMED tags (from report)
- Positive entities (from report)
- Country of residence (from address)
- Imputed Race / Imputed Ethnicity (derived fields)
Privacy-first DICOM De-identification Methods
The dataset uses cryptographic hashing & pseudonymization to comply with HIPAA while preserving clinical utility and protecting sensitive data.
Pixel-level Protection
Redaction/pseudonymization of burned-in text and de-facing when needed.
Metadata Protection
Safe Harbor identifiers anonymized, while standard DICOM metadata is preserved.
Date Shifting
Dates are shifted within a 365-day range, at the patient level to preserve temporal relationships across studies.
Demographic Flooring
Certain fields are capped/floored to reduce re-identification risk (e.g., age, weight, size, and some ethnicity values).
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Frequently Asked Questions (FAQ)
1. What is a DICOM image dataset?
A DICOM image dataset is a collection of medical imaging studies stored in the DICOM standard, including pixel data and clinical metadata, commonly used to train and validate healthcare AI models.
2. What’s included in this DICOM Image Dataset?
Depending on licensing scope, it can include DICOM pixel data, preserved (de-identified) DICOM metadata, optional study reports, and optional value-added custom metadata.
3. Are the images de-identified?
Yes. Images are de-identified at the pixel level, including redaction/pseudonymization of text on imagery and de-facing when needed.
4. Is the DICOM metadata preserved?
Standard DICOM metadata is preserved for delivery, while HIPAA Safe Harbor identifiers are anonymized (e.g., patient/institution identifiers and dates).
5. How are dates handled?
Dates can be shifted within 365 days, applied consistently at the patient level to preserve relative timing across studies.
6. Are radiology/study reports included?
When available and licensed, study reports (unstructured narrative text) can be included, with identifiers pseudonymized.
7. What custom metadata can be available?
Options can include parsed patient age, SNOMED tags, positive entities, country of residence, and other derived fields.
8. Can I request a specific cohort (modality, body part, geography, etc.)?
Yes—share your target scope and filters, and Shaip will propose the best-fit dataset slice based on availability.
9. How do I license the dataset?
Submit your requirements via the Contact Us form. Our team will confirm availability, scope, licensing terms, and delivery options.