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

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 classification. Evaluation of multiple deep learning models revealed that an end-to-end ConvNeXt network using low-resolution IHC images achieved an F1 score of 83.52%, representing an improvement of 5.35% over patch-based methods. Class-wise F1 scores were 75.6% for HER2-0, 82.4% for HER2-low, and 91.5% for HER2-high, indicating the challenge in distinguishing HER2-0 and HER2-low cases. This study highlights the potential of simple yet effective deep learning techniques to significantly improve accuracy and reproducibility in breast cancer classification, supporting their integration into clinical workflows for better patient outcomes.