Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation.

Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings.