The paper presents a deep learning-based approach for identifying metastatic breast cancer in whole slide images (WSIs) of sentinel lymph node biopsies. The team won both the slide-based classification and tumor localization tasks in the Camelyon16 grand challenge, achieving an AUC of 0.925 and a score of 0.7051, respectively. When combined with a human pathologist's diagnoses, the system's AUC increased to 0.995, reducing the error rate by approximately 85%. The approach uses a deep convolutional neural network (DCNN) to train on millions of patches, generating tumor probability heatmaps for slide-based classification and lesion localization. The method involves image preprocessing to focus on regions likely to contain cancer, and post-processing to refine predictions. The results demonstrate the significant improvement in accuracy and the potential for integrating deep learning into clinical workflows to enhance diagnostic precision and reduce cognitive load.The paper presents a deep learning-based approach for identifying metastatic breast cancer in whole slide images (WSIs) of sentinel lymph node biopsies. The team won both the slide-based classification and tumor localization tasks in the Camelyon16 grand challenge, achieving an AUC of 0.925 and a score of 0.7051, respectively. When combined with a human pathologist's diagnoses, the system's AUC increased to 0.995, reducing the error rate by approximately 85%. The approach uses a deep convolutional neural network (DCNN) to train on millions of patches, generating tumor probability heatmaps for slide-based classification and lesion localization. The method involves image preprocessing to focus on regions likely to contain cancer, and post-processing to refine predictions. The results demonstrate the significant improvement in accuracy and the potential for integrating deep learning into clinical workflows to enhance diagnostic precision and reduce cognitive load.