vision
RAD-DINO
Microsoft chest X-ray image encoder trained with DINOv2 self-supervision, for radiology classification, segmentation and report generation.
Levelmodel
Classvision
Parameters86.58M
Contextnot in the notes
Released17 May 2024
Opennessunknown
Runs onnot in the notes
Statusactive
R&D Topics
Identity
- Developer
- Microsoft AI
- Family
- not in the notes
- Released
- 17 May 2024 sourcesregistry date
- Weights published
- –
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- dense transformer
- Total parameters
- 86.58M sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- image
- Output
- not in the notes
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- unknown
- Licence
- not in the notes sources
- Access routes
- open weights download, cloud marketplace
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
DeveloperMicrosoft AIbig tech, USA
Microsoft AIbig tech, USAModelRAD-DINOMicrosoft AI, vision
RAD-DINOMicrosoft AI, visionR&D TopicsMedical Image Segmentation and Screening AI (Radiology)Self-Supervised Visual Representation Learning
Use CasesMedical Image SegmentationHealthcareChest X-Ray and TB ScreeningHealthcareMammography Cancer DetectionHealthcareEye ScreeningHealthcare+2 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
Kementerian Kesehatan (Kemenkes), national TB programmeIn production, 13 Aug 2026Kemenkes TB programme with UGM, University of Melbourne, SAPDA, YPKMPResearch, 3 Aug 2026Kemenkes; RS Fatmawati; RSPONPilot, 27 Feb 2025Kemenkes; RSUP Dr. M. Djamil PadangPilot, 9 Jul 2024+6 moreDeployments of these use cases; none names this model.
Kementerian Kesehatan (Kemenkes), national TB programmeIn production, 13 Aug 2026Kemenkes TB programme with UGM, University of Melbourne, SAPDA, YPKMPResearch, 3 Aug 2026Kemenkes; RS Fatmawati; RSPONPilot, 27 Feb 2025Kemenkes; RSUP Dr. M. Djamil PadangPilot, 9 Jul 2024+6 moreDeployments of these use cases; none names this model.Organisations 1
R&D topics 2
Self-Supervised Visual Representation LearningPretraining image encoders without labels so their features transfer to many vision tasks.produced byMedical Image Segmentation and Screening AI (Radiology)Models that outline organs and lesions or flag disease on X-ray, CT, MRI and other scans.produced by
Sources
- huggingface.co/microsoft/rad-dino https://huggingface.co/microsoft/rad-dino
- ai.azure.com/catalog/models?industry=health-and-life-scien... https://ai.azure.com/catalog/models?industry=health-and-life-sciences
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "RAD-DINO", https://aurion.semai.id/models/microsoft-rad-dino/, records of October 2026.