Stain SAN: simultaneous augmentation and normalization for histopathology images.
Taebin KimYao LiBenjamin C CalhounAatish ThennavanLisa Anne CareyW Fraser SymmansMelissa A TroesterCharles M PerouJames Stephen MarronPublished in: Journal of medical imaging (Bellingham, Wash.) (2024)
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.