Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor-associated stroma as a diagnostic biomarker.

使用卷积神经网络分析乳腺癌患者的肿瘤相关基质来诊断，该系统在处理了646例乳腺组织活检标本后被证明具有较强的诊断能力，达到了0.92的ROC曲线下面积。

利用基于深度学习的评估肿瘤相关基质来诊断组织病理学图像中的乳腺癌