Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology.

Nicolas Nerrienet¹, Rémy Peyret¹, Marie Sockeel¹, Stéphane Sockeel¹

¹Primaa, Paris, France.

Abstract

PURPOSE: Generalization is one of the main challenges of computational pathology. Slide preparation heterogeneity and the diversity of scanners lead to poor model performance when used on data from medical centers not seen during training. In order to achieve stain invariance in breast invasive carcinoma patch classification, we implement a stain translation strategy using cycleGANs for unsupervis…

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