Ensemble Network for Region Identification in Breast Histopathology Slides

Accurate analysis of tissue structures in breast cancer histopathology slides is crucial for staging treatments and predicting outcome. Such analysis depends on identification of tissue architecture in different regions, and determining the different types of cancer morphology which includes in-situ carcinoma, invasive tumor, and benign tumor. We propose an automated classification method for identifying these micro-architectures using an ensemble of convolutional neural networks. This ensemble is constructed by combining multiple networks, trained using different data subset sampling and image perturbation models. Our proposed approach results in a high performing detector with robustness to data variations.