vision
MedSigLIP
400M medical image encoder derived from SigLIP-400M, part of the MedGemma release.
Levelmodel
Classvision
Parameters400M
Contextnot in the notes
Releasednot in the notes
Opennessunknown
Runs onnot in the notes
Statusnot in the notes
Lineage
finetune
SigLIP-400M is the parent (finetune)high confidencenotes
Where this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 2 models. Confidence and evidence are the notes' own.
R&D Topics
Identity
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
- not in the notes
- Total parameters
- 400M 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
- not in the notes
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.
Industry Use Cases
No industry use case links this model.
Links
DeveloperGooglebig tech, USA
Googlebig tech, USAModelMedSigLIPGoogle, vision
MedSigLIPGoogle, visionR&D TopicsContrastive Image-Text Encoders (CLIP and SigLIP Lines)Medical Image Segmentation and Screening AI (Radiology)
Use CasesMedical Image SegmentationHealthcareChest X-Ray and TB ScreeningHealthcareMammography Cancer DetectionHealthcarePathology Whole-Slide ImagingHealthcare+3 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
Contrastive Image-Text Encoders (CLIP and SigLIP Lines)Image and text encoders trained together so that pictures and captions share one embeddin…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
- MedGemma Technical Report arxiv.org https://arxiv.org/html/2507.05201v2
- google/medgemma-27b-text-it , Hugging Face huggingface.co https://huggingface.co/google/medgemma-27b-text-it
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "MedSigLIP", https://aurion.semai.id/models/google-medsiglip/, records of October 2026.