Self-HER2Net: A generative self-supervised framework for HER2 classification in IHC histopathology of breast cancer

Breast cancer is a significant global health concern, where precise identification of proteins like Human Epidermal Growth Factor Receptor 2 (HER2) in cancer cells via Immunohistochemistry (IHC) is pivotal for treatment decisions. HER2 overexpression is evaluated through HER2 scoring on a scale from 0 to 3 + based on staining patterns and intensity. Recent efforts have been made to automate HER2 scoring using image processing and AI techniques. However, existing methods require large manually annotated datasets as these follow supervised learning paradigms. Therefore, we proposed a generative self-supervised learning (SSL) framework "Self-HER2Net" for the classification of HER2 scoring, to reduce dependence on large manually annotated data by leveraging one of best performing four novel generative self-supervised tasks, that we proposed. The first two SSL tasks HER2_hsl and HER2_hsv are domain-agnostic and the other two HER2_dab and HER2_hae are domain-specific SSL tasks focusing on domain-agnostic and domain-specific staining patterns and intensity representation. Our approach is evaluated under different budget scenarios (2 %, 15 %, & 100 % labeled datasets) and also out distribution test. For tile-level assessment, HER2_hsv achieved the best performance with AUC-ROC of 0.965 ± 0.037. Our self-supervised learning approach shows potential for application in scenarios with limited annotated data for HER2 analysis.