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。