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.

使用深度学习方法对免疫组织化学染色的乳腺癌组织图像进行 HER2 状态自动分类，以提高诊断准确性和评估速度，对乳腺癌的治疗规划可能产生重大影响。