Knowledge-guided semantic indexing of breast cancer histopathology images

Narrowing the semantic gap represents one of the most outstanding challenges in medical image analysis and indexing. This paper introduces a medical knowledge - guided paradigm for semantic indexing of histopathology images, applied to breast cancer grading (BCG). Our method improves pathologists' current manual procedures consistency by employing a semantic indexing technique, according to a rule-based decision system related to Nottingham BCG system. The challenge is to move from the medical concepts/rules related to the BCG, to the computer vision (CV) concepts and symbolic rules, to design a future generic framework-following Web Ontology Language standards - for an semi-automatic generation of CV rules. The effectiveness of this approach was experimentally validated over six breast cancer cases consisting of 7000 frames with domain knowledge from experts of Singapore National University Hospital, Pathology Department. Our method provides pathologists a robust and consistent tool for BCG and opens interesting perspectives for the semantic retrieval and visual positioning.