New article presenting SKET – The Semantic Knowledge Extractor Tool

Our article entitled “Empowering Digital Pathology Applications through Explainable Knowledge Extraction Tools” has been published in Journal of Pathology Informatics.

The Semantic Knowledge Extractor Tool (SKET) is an unsupervised hybrid system that combines rule-based techniques with pre-trained machine learning models to extract key pathological concepts from diagnostic reports. SKET is a viable solution to reduce pathologists’ workload and can be used as a first, cheap solution to bootstrap supervised models in absence of manual annotations.

SKET architecture.

The SKET eXplained (SKET X) is a web-based system that supports pathologists and domain experts in the visual understanding of SKET predictions. SKET X can refine parameters and rules over time, thus improving the system effectiveness and increasing user’s trust and confidence

SKET X dashboard providing information about the executed SKET pipelines