Classification of HER2 score in breast cancer images using deep learning and pyramid sampling
Şahan Yoruç Selçuk, Xilin Yang, Bijie Bai, Yijie Zhang, Yuzhu Li, Musa Aydın, Aras Firat Unal, Aditya Gomatam, Zhen Guo, Morgan Angus Darrow, Goren Kolodney, Karine Atlan, Tal Keidar Haran, Nir Pillar, Aydogan Özcan · 2024
We introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained breast cancer tissue images. Our deep learning-based method leverages pyramid sampling to analyze features across multiple scales from IHC-stained breast tissue images, managing the computational load effectively and addressing the challenges of HER2 expression heterogeneity by capturing detailed cellular features and broader tissue architecture. Upon application to 523 core images, the model achieved a classification accuracy of 85.47%, demonstrating the ability to counteract staining variability and tissue heterogeneity, which might improve the accuracy and timeliness of breast cancer treatment planning.