Towards a Deeper Understanding of How a Pathologist Makes a Diagnosis: Visualization of the Diagnostic Process in Histopathology Advancements in Artificial Intelligence (AI) and Machine Learning (ML) are enabling new diagnostic capabilities. In this paper we argue that the very first step before introducing AIML into diagnostic workflows is a deep understanding of how pathologists work. To contribute to a deeper understanding of the diagnostic process in histopathology, we developed a visualization concept, including: (a) the sequence of the views observed by the pathologist (Observation Path), (b) the sequence of the spoken comments and statements of the pathologist (Dictation Path), (c) the underlying knowledge and experience of the pathologist (Knowledge Path), (d) information about the current phase of the diagnostic process and (e) the current magnification factor of the microscope chosen by the pathologist. We implemented the proofofconcept prototype as HTML5CSS3JavaScript application.

NLP for the Generation of Training Data Sets for OntologyGuided WeaklySupervised Machine Learning in Digital Pathology The combination of ontologies with machine learning (ML) approaches is a hot topic and not yet extensively investigated but having great future potential. This is due to the general fact that both, ontologies and ML, constitute two indispensable technologies for domainspecific knowledge extraction, actively used in knowledgebased systems. Whilst the primary goal of both these approaches are the same, knowledge discovery, little is yet known about how the two sources of knowledge can be successfully integrated. The main data source in digital pathology are whole slide images. For the effective generation of sufficiently large and highquality training data we need to extract in addition information from medical reports, containing nonstandardized text. Since full annotation on pixel level would be impracticably expensive, a practical solution is in weaklysupervised ML. In the project described in this paper we used ontologyguided natural language processing (NLP) for term extraction and a decision tree built with an expertcurated classification system. This demonstrates the practical value of our solution to analyze and structure training data sets for ML and as a tool for the generation of biobank catalogues.