Deep learning is a state-of-the-art pattern recognition technique that has proven extremely powerful for the analysis of digitized histopathological slides. In our work, we studied the use of deep learning to assess a range of breast-cancer related tissue features: presence of lymph node metastases, extend of lymphatic infiltrate within tumors, and the components of tumor grading. It was shown that DL enables reproducible, quantitative tumor feature extraction, showing a good correlation with pathologists’ scores and with patient outcome. Our current research involves larger scale validation with pathologists, to study the added value of the developed algorithms in routine practice, in terms of efficiency and diagnostic accuracy. Such studies will be required for certification but are still mostly lacking in our field, making it difficult to assess the true potential of deep learning for pathology diagnostics.