Adversarial domain adaptation to improve automatic breast cancer grading in lymph nodes

The progression of breast cancer can be quantified in whole-slide images of lymph nodes. We describe a novel deep learning method for classification of whole-slide images and patient level breast cancer grading. Our method is based on domain adaptation using a Cycle-Consistent Generative Adversarial Network (CycleGAN), in conjunction with a densely connected deep neural network. Our method performs classification on small image patches and uses model averaging for boosting. The classification results are used to determine a slide level class and are further aggregated to predict a patient level grade. Our method was applied to the challenging CAMELYON17 dataset. It turned out that domain adaptation improves the result compared to state-of-the-art data augmentation. The fast processing speed of our method enables high-throughput image analysis.