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Automated classification of histopathological whole-slide images (WSI) ofbreast tissue requires analysis at very high resolutions with a largecontextual area. In this paper, we present context-aware stacked convolutionalneural networks (CNN) for classification of breast WSIs into normal/benign,ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We firsttrain a CNN using high pixel resolution patches to capture cellular levelinformation. The feature responses generated by this model are then fed asinput to a second CNN, stacked on top of the first. Training of this stackedarchitecture with large input patches enables learning of fine-grained(cellular) details and global interdependence of tissue structures. Our systemis trained and evaluated on a dataset containing 221 WSIs of H&E stained breasttissue specimens. The system achieves an AUC of 0.962 for the binaryclassification of non-malignant and malignant slides and obtains a three classaccuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC,demonstrating its potentials for routine diagnostics.

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images