Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and
  Pyramid Sampling
Sahan Yoruc Selcuk Xilin Yang Bijie Bai Yijie Zhang Yuzhu Li Musa Aydin Aras Firat Unal Aditya Gomatam Zhen Guo Darrow Morgan Angus Goren Kolodney Karine Atlan Tal Keidar Haran Nir Pillar Aydogan Ozcan
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. Accurate assessment of immunohistochemically (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. Here, we introduce a deep learning-based approach
utilizing pyramid sampling for the automated classification of HER2 status in
IHC-stained BC tissue images. Our approach 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. This method 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.
Our automated system, proving reliable as an adjunct pathology tool, has the
potential to enhance diagnostic precision and evaluation speed, and might
significantly impact cancer treatment planning.