In this talk, we will provide an introduction to computational pathology, which is an emerging cross-discipline between pathology and computer engineering. Besides, we will introduce a deep learning-based automatic whole slide image analysis system for the identification of cancer metastases in breast sentinel lymph nodes. Our system won the 1st position in the International Challenge: Camelyon16, which was held at the International Symposium on Biomedical Imaging (ISBI) 2016. The system achieved an area under the receiver operating curve (AUC) of 0.925 for the task of whole slide image classification and an average sensitivity of 0.705 for the tumor localization task. A pathologist independently reviewed the same images, obtaining a whole slide image classification AUC of 0.966 and a tumor localization score of 0.733. By combining the predictions from the human pathologist and the automatic analysis system, the performance becomes even higher. These results demonstrate the power of using deep learning to produce significant improvements in the accuracy of pathological diagnoses.