Fine-grained multiclass nuclei segmentation with molecular empowered all-in-SAM model
Our approach not only reduces the workload for annotators but also extends the accessibility of precise biomedical image analysis to resource-limited settings, thereby advancing medical diagnostics and automating pathology image analysis.
Authors
- Can Cui (ORCID: https://orcid.org/0000-0002-2159-5387)
- Ruining Deng (ORCID: https://orcid.org/0000-0001-6300-8518)
- Yucheng Tang (ORCID: https://orcid.org/0000-0002-6008-9700)
- Shunxing Bao (ORCID: https://orcid.org/0000-0001-6376-4292)
- Naweed I. Chowdhury (ORCID: https://orcid.org/0000-0001-9826-4917)
- Tianyuan Yao (ORCID: https://orcid.org/0000-0002-1848-079X)
- Yuankai Huo (ORCID: https://orcid.org/0000-0002-2096-8065)
- Xueyuan Li (ORCID: https://orcid.org/0000-0002-0903-9388)
- Quan Liu
- Haichun Yang
Institutions
- Nvidia (United Kingdom) (GB)
- Vanderbilt University (US)
- Nvidia (United States) (US)
- Vanderbilt Health (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1117/1.jmi.12.5.057501
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- U.S. Department of Defense
- Vanderbilt University
- Leona M. and Harry B. Helmsley Charitable Trust
- Nvidia
- National Institutes of Health