Abstract:
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 ...Show MoreMetadata
Abstract:
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 domain-specific knowledge extraction, actively used in knowledge-based 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 high-quality training data we need to extract in addition information from medical reports, containing non-standardized text. Since full annotation on pixel level would be impracticably expensive, a practical solution is in weakly-supervised ML. In the project described in this paper we used ontology-guided natural language processing (NLP) for term extraction and a decision tree built with an expert-curated 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.
Date of Conference: 29 June 2019 - 03 July 2019
Date Added to IEEE Xplore: 27 January 2020
ISBN Information: