Cell type prediction with neighborhood-enhanced cellular embedding using deep learning on hematoxylin and eosin-stained images
Deep learning models accurately predicted cell types in breast and colon cancer datasets using only cell morphology and neighborhood embedding.
Authors
- Hanzhou Wang (ORCID: https://orcid.org/0000-0002-0493-827X)
- Tapsya Nayak (ORCID: https://orcid.org/0000-0001-8149-3916)
- Yu‐Chiao Chiu (ORCID: https://orcid.org/0000-0003-1647-8634)
- Yufei Huang (ORCID: https://orcid.org/0000-0001-6268-5357)
- Nam Nhut Phan (ORCID: https://orcid.org/0000-0001-7798-8190)
- Yidong Chen (ORCID: https://orcid.org/0000-0001-6806-2185)
- Zhenqing Ye
- Yu-Fang Jin
Institutions
- The University of Texas at San Antonio Health Science Center (US)
- University of Pittsburgh (US)
- UPMC Hillman Cancer Center (US)
- The University of Texas at San Antonio (US)
Publication Details
- Journal
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.csbj.2025.07.026
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- NIH Office of the Director
- National Cancer Institute