Hierarchically supervised computational pathology stratifies HER2 categories and ERBB2 amplification risk from routine H&E slides in breast cancer
The paper introduces CHERISH, a hierarchically supervised computational pathology framework that leverages routine H&E slides to accurately stratify HER2 categories and predict ERBB2 amplification risk by embedding clinical testing logic into its architecture, thereby overcoming the limitations of conventional models and reducing the need for resource-intensive confirmatory testing.