Contrasting low- and high-resolution features for HER2 scoring using deep learning.

ABSTRACT: Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Among immunohistochemistry (IHC) biomarkers, HER2 plays a critical role in guiding therapy decisions. In particular, a recent clinical trial has shown that 3-way classification of HER2 (0, low, and high) using IHC is essential for identifying patients with HER2 low expression who may benefit from new targeted therapies. However, traditional IHC classification relies on the expertise of pathologists, making it labor-intensive and prone to significant inter-observer variability. To address these challenges, this study introduces the India Pathology Breast Cancer Dataset, comprising HER2 IHC slides from 500 patients, with a primary focus on automating 3-way HER2 classi

SUBMITTER: Chauhan E 

PROVIDER: S-EPMC12723012 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature