Transformer-Based <i>HER2</i> Scoring in Breast Cancer: Comparative Performance of a Foundation and a Lightweight Model.

ABSTRACT: Background/Objectives: Human epidermal growth factor 2 (HER2) scoring is critical for modern breast cancer therapies, especially with emerging indications of antibody-drug conjugates for HER2-low tumors. However, inter-observer agreement remains limited in borderline cases. Automatic artificial intelligence-based scoring has the potential to improve diagnostic consistency and scalability. This study aimed to develop two transformer-based models for HER2 scoring of breast cancer whole-slide images (WSIs) and compare their performance. Methods: We adapted a large-scale foundation model (Virchow) and a lightweight model (TinyViT). Both were trained using patch-level annotations and integrated into a WSI scoring pipeline. Performance was evaluated on a clinic

SUBMITTER: Wang YH 

PROVIDER: S-EPMC12428388 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature