  This paper presents U-net based breast cancer metastases detection and
classification in lymph nodes, as well as patient-level classification based on
metastases detection. The whole pipeline can be divided into five steps:
preprocessing and data argumentation, patch-based segmentation, post
processing, slide-level classification, and patient-level classification. In
order to reduce overfitting and speedup convergence, we applied batch
normalization and dropout into U-Net. The final Kappa score reaches 0.902 on
training data.
