  Predicting TNM stage is the major determinant of breast cancer prognosis and
treatment. The essential part of TNM stage classification is whether the cancer
has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage
(pN-stage) is commonly performed by pathologists detecting metastasis in
histological slides. However, this diagnostic procedure is prone to
misinterpretation and would normally require extensive time by pathologists
because of the sheer volume of data that needs a thorough review. Automated
detection of lymph node metastasis and pN-stage prediction has a great
potential to reduce their workload and help the pathologist. Recent advances in
convolutional neural networks (CNN) have shown significant improvements in
histological slide analysis, but accuracy is not optimized because of the
difficulty in the handling of gigapixel images. In this paper, we propose a
robust method for metastasis detection and pN-stage classification in breast
cancer from multiple gigapixel pathology images in an effective way. pN-stage
is predicted by combining patch-level CNN based metastasis detector and
slide-level lymph node classifier. The proposed framework achieves a
state-of-the-art quadratic weighted kappa score of 0.9203 on the Camelyon17
dataset, outperforming the previous winning method of the Camelyon17 challenge.
