A deep learning approach was developed for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. The system used millions of training patches to train a deep convolutional neural network to classify tumor and normal patches. The model then generated tumor probability heatmaps, which were post-processed to make predictions for slide-based classification and tumor localization tasks. The system achieved an area under the receiver operating characteristic (AUC) of 0.925 for slide classification and a score of 0.7051 for tumor localization in the Camelyon Grand Challenge 2016. When combined with a human pathologist's diagnoses, the system significantly reduced the pathologist's error rate, increasing their AUC to 0.995, representing an 85% reduction in error rate. The Camelyon16 dataset consisted of 400 whole slide images, split into 270 for training and 130 for testing. Ground truth data was provided in two formats: XML files with annotated contours and binary masks. The system's performance was evaluated using slide-based and lesion-based metrics. For slide-based evaluation, the system achieved an AUC of 0.925, and for lesion-based evaluation, it achieved a score of 0.7051, the highest in the competition. The system's approach included image preprocessing to identify tissue regions and reduce background noise. A patch-based classification stage was used to train a deep neural network, followed by post-processing to generate heatmaps and make final predictions. The system used GoogLeNet as its deep learning model due to its efficiency and stability. The model was trained on various magnification levels, with 40x magnification yielding the best performance. The system's results demonstrated the effectiveness of deep learning in improving the accuracy of pathological diagnoses. Combining deep learning with human pathologists significantly reduced error rates, highlighting the potential of integrating deep learning into diagnostic workflows to enhance the reproducibility, accuracy, and clinical value of pathological diagnoses.A deep learning approach was developed for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies. The system used millions of training patches to train a deep convolutional neural network to classify tumor and normal patches. The model then generated tumor probability heatmaps, which were post-processed to make predictions for slide-based classification and tumor localization tasks. The system achieved an area under the receiver operating characteristic (AUC) of 0.925 for slide classification and a score of 0.7051 for tumor localization in the Camelyon Grand Challenge 2016. When combined with a human pathologist's diagnoses, the system significantly reduced the pathologist's error rate, increasing their AUC to 0.995, representing an 85% reduction in error rate. The Camelyon16 dataset consisted of 400 whole slide images, split into 270 for training and 130 for testing. Ground truth data was provided in two formats: XML files with annotated contours and binary masks. The system's performance was evaluated using slide-based and lesion-based metrics. For slide-based evaluation, the system achieved an AUC of 0.925, and for lesion-based evaluation, it achieved a score of 0.7051, the highest in the competition. The system's approach included image preprocessing to identify tissue regions and reduce background noise. A patch-based classification stage was used to train a deep neural network, followed by post-processing to generate heatmaps and make final predictions. The system used GoogLeNet as its deep learning model due to its efficiency and stability. The model was trained on various magnification levels, with 40x magnification yielding the best performance. The system's results demonstrated the effectiveness of deep learning in improving the accuracy of pathological diagnoses. Combining deep learning with human pathologists significantly reduced error rates, highlighting the potential of integrating deep learning into diagnostic workflows to enhance the reproducibility, accuracy, and clinical value of pathological diagnoses.