Resumen
This study proposes an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images, which is crucial for fully automated immunohistochemistry quantification. The proposed method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional staining slides. The model demonstrated an impressive intersection over union score on the test set, and a fully automated Ki-67 scoring system based on the model?s predictions exhibited high consistency with the scores given by experienced pathologists. The proposed method brings the breast cancer fully immunohistochemistry quantitative scoring system one step closer to clinical application.