Stain SAN: simultaneous augmentation and normalization for histopathology images
Stain SAN emerges as a promising solution, addressing the potential shortcomings of contemporary stain adaptation methods. Its effectiveness is underscored by notable improvements in the context of clinical estrogen receptor status classification, where it achieves the best AUC performance. The findings endorse Stain SAN as a robust approach for stain domain adaptation in histopathology images, with implications for advancing computational tasks in the field.
Authors
- W. Fraser Symmans (ORCID: https://orcid.org/0000-0002-1526-184X)
- Aatish Thennavan
- Lisa A. Carey (ORCID: https://orcid.org/0000-0003-2388-4649)
- Charles M. Perou (ORCID: https://orcid.org/0000-0001-9827-2247)
- Melissa A. Troester (ORCID: https://orcid.org/0000-0001-9506-6624)
- Benjamin C. Calhoun (ORCID: https://orcid.org/0000-0001-6505-5430)
- Yao Li (ORCID: https://orcid.org/0000-0002-7195-5774)
- Taebin Kim (ORCID: https://orcid.org/0009-0009-5901-6844)
- J. S. Marron
Institutions
- University of North Carolina at Chapel Hill (US)
- The University of Texas MD Anderson Cancer Center (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2024-08-23
- DOI
- https://doi.org/10.1117/1.jmi.11.4.044006
- Citations
- 4
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Science Foundation
- Susan G. Komen for the Cure
- Breast Cancer Research Foundation
- Cisco Systems
- National Institutes of Health
- National Cancer Institute