Plug‐and‐play segment anything model improves nnUNet performance
A comprehensive evaluation of multiple small-sample segmentation tasks demonstrates significant improvements in segmentation performance by nnSAM, highlighting the vast potential of small-sample learning.
Authors
- Bowen Jing
- You Zhang (ORCID: https://orcid.org/0000-0002-8033-2755)
- Jing Wang (ORCID: https://orcid.org/0000-0002-0639-6419)
- Zihan Li (ORCID: https://orcid.org/0009-0004-3839-0611)
- Yunxiang Li (ORCID: https://orcid.org/0000-0003-0622-4710)
Institutions
- University of Washington (US)
- Southwestern Medical Center (US)
- Southwestern Medical Center (US)
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1002/mp.17481
- Citations
- 32
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 10.77
Funders
- National Institutes of Health