基于深度学习与金字塔采样的乳腺癌图像HER2评分自动化
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
AI总结：
提出基于金字塔采样的深度学习方法，自动分类IHC染色乳腺癌图像中的HER2状态，在523个核心图像数据集上达到84.70%盲测准确率，可作为辅助病理工具提升诊断精度与速度。
摘要：
人表皮生长因子受体2（HER2）是癌细胞生长中的关键蛋白，标志着乳腺癌（BC）的侵袭性，并有助于预测其预后。对免疫组织化学（IHC）染色的组织切片中HER2表达水平的准确评估，对于治疗指导和理解癌症机制都至关重要。然而，由委员会认证的病理学家进行人工检查的传统工作流程面临挑战，包括观察者间和观察者内的不一致性以及较长的周转时间。在此，我们介绍一种基于深度学习的方法，利用金字塔采样对IHC染色的BC组织图像中的HER2状态进行自动分类。我们的方法在不同空间尺度上分析形态学特征，有效管理计算负载，并促进对细胞和更大尺度组织层面细节的详细检查。该方法通过提供全面的视图来解决HER2表达的异质性，在包含523个来自组织微阵列的核心图像的数据集上，实现了84.70%的盲测分类准确率。我们的自动化系统作为辅助病理工具被证明是可靠的，有潜力提高诊断精度和评估速度，并可能对癌症治疗计划产生显著影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
