MedSAM3: Delving into Segment Anything with Medical Concepts
Quick summary
arXiv:2511.19046v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application. Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation. By fine-tuning the Segment Anything Model (SAM) 3 architecture on medical images paired with semantic conceptual labels, our MedSAM-3 enables medical Promptable Concept Segmentation (PCS), allowing precise targeting of anatomical struct
Key takeaways
- arXiv:2511.19046v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental for biomedical discovery.
- Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application.
- Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation.
Why it matters
“MedSAM3: Delving into Segment Anything with Medical Concepts” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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