Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
This addresses the problem of improving diagnostic accuracy in breast cancer pathology for medical professionals, representing an incremental advancement in deep learning applications for histopathology.
The paper tackles automated classification of breast cancer in whole-slide histopathology images by proposing a context-aware stacked convolutional neural network, achieving an AUC of 0.962 for binary malignant classification and 81.3% accuracy for three-class categorization.
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global interdependence of tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of non-malignant and malignant slides and obtains a three class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potentials for routine diagnostics.