A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics
COIN represents a robust and privacy-preserving framework for scalable cytology data generation. Its ability to synthesize realistic images and enhance AI diagnostics highlights its broad applicability in computational pathology, providing a valuable tool to accelerate the development and implementation of AI-based diagnostic solutions.
Authors
- Muyan Cai (ORCID: https://orcid.org/0000-0002-4646-4391)
- Q.L. Chen (ORCID: https://orcid.org/0009-0009-0568-7351)
- Junpeng Lai (ORCID: https://orcid.org/0009-0008-4589-4411)
- Xueyi Zheng (ORCID: https://orcid.org/0000-0003-1386-7052)
- S. Chen (ORCID: https://orcid.org/0009-0003-5893-8595)
- Dan Xie (ORCID: https://orcid.org/0000-0003-2242-3138)
- Ruixuan Wang (ORCID: https://orcid.org/0000-0002-8714-0369)
- Ke Zheng (ORCID: https://orcid.org/0000-0002-4558-6039)
- Xinke Zhang (ORCID: https://orcid.org/0000-0002-3527-2140)
- J. Wang (ORCID: https://orcid.org/0000-0003-1291-9922)
- Sha Fu (ORCID: https://orcid.org/0009-0001-4334-7296)
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen Memorial Hospital (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- Clinical Cancer Research
- Published
- 2025-12-04
- DOI
- https://doi.org/10.1158/1078-0432.ccr-25-2445
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00