Digital Pathologist uses image analysis to extract quantitative features from digitized pathology slides to predict cancer survival. It segments images into epithelial and stromal regions and measures thousands of morphological features. These include standard metrics like nuclear size as well as higher-level relationships between image objects. Models are trained on annotated slides from cancer patients with known survival outcomes. The system was able to build a prognostic model from one dataset and validate it on a separate, independent dataset, identifying novel image-based features associated with survival in the process. This automated digital pathology approach could help standardize quantitative analysis and uncover new biological insights compared to traditional visual examination alone.