Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings
Ultrasound is the most widely deployed imaging modality worldwide, yet clinical AI remains fragmented into narrow single-task models that fail when device, operator, or anatomy changes. Here we present SonoCorpus, an open resource unifying 456,963 images and 1,626,085 expert masks from 53 public datasets spanning 24 clinical applications and 17 countries, and SonoBase, an interactive segmentation foundation model pretrained on it. Across fifteen evaluation datasets introducing new organs, devices, operators, and geographies, SonoBase outperforms SAM2, MedSAM2, and the concept-promptable MedSAM3 on every dataset and matches per-dataset specialist models trained on the same data; on fully e...
| Authors | Chao Qin, Fahad Shahbaz Khan, Salman Khan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
Connected in the knowledge graph
- AlfredQin/sonobaseModel · cited by
Models and datasets referencing this paper
- AlfredQin/sonobaseModel
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.