Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling

Abstract

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. Results: This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning.